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International
Conference on Statistical Distributions and Applications Oct. 10-12, 2019, at Eberhard Conference
Center, Grand Rapids, MI, USA |
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(Expired) |
Updated
10/07/2019
An Updated Program is now
posted below
A complete program by Time and By
Alphabet order is available HERE
The Conference Floor Plan and
Area Map is available HERE
Thank you for your
participation in the ICOSDA 2019. All of the presentations will be on October
11 and 12. We will have two full day of outstanding programs covering a wide
variety of topics. The following are important information to help you get ready for the
conference.
For all oral presenters,
including Keynotes, Plenary, Topic-invited and General-invited speakers: ·
Each presentation room will be equipped with a PC laptop computer to
host your presentations (in PPT or PDF format). ·
Please note the Conference
Center is not be able to support MAC computer. If you plan to use your own
MAC computer, you will also need to bring the proper cable to connect to the
projection system. ·
Each oral presenter is
asked to prepare and bring your own presentation in either PPT or PDF format
in a USB Type 1 or Type 2 flesh drive. (Not a Type C USB) ·
Please always bring a
backup presentation file with you, ·
On the day, prior to your presentation, bring your flesh drive and
upload your presentation to the laptop
in your presentation room, and make sure your presentation can be open
properly before the session starts. For student poster
presentations: ·
Poster Presentations will be on the Hallway of 2nd floor
of the conference center. ·
An easel and a
presentation board of 5’(width)x3.5’(height) will be available in the poster display area. ·
Student presenters are asked to prepare their posters, which will be
displayed on the presentation board. ·
A name card is attached to the display board. Presenters are asked to
display their posters prior to the break before 5:40 pm on October 11. ·
The time for poster presentation and discussion is from 5:40 pm to
6:40 pm on October 11. |
Eberhard Center 301 Fulton St. Grand Rapids, MI 49504 |
Conference Registration Desk on 2nd
Floor |
The general program
schedule is posted below. Please carefully check your
presentation schedule and the session(s) you will be chairing. The
abstracts are also posted on the ‘Accepted Abstract’ Page. Please take a look
at your title and abstract. If you notice any errors, please e-mail carl.lee@cmich.edu.
Schedule Summary Table (Gi: General-Invited Session,
TIi: Topic-Invited Session. See the session descriptions following this table.)
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Keynotes and Plenary Sessions
All Keynotes and Plenary
Sessions are in General Session Room: Room #215
(B,C,D,E,F,G)
Date |
Time |
Session |
Chair/Speaker |
Title |
10/11/2019 |
8:00 - 9:00 |
KY_1_(Chair) |
Famoye, Felix |
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KY_1_Speaker |
Banks, David |
Adversarial Risk Analysis |
10/11/2019 |
19:30 - 20:30 |
KY_2_(Chair) |
Sepanski, Jungsywan |
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KY_2_Speaker |
Yi, Grace |
Making Sense of Noisy
Data: Some Issues and Discussions |
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10/12//2019 |
8:00 -9:00 |
KY_3_(Chair) |
Lee, Carl |
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KY_3_Speaker |
Vander Wiel, Scott |
Fitting Stress-Strain
Fields in Polycrystalline Materials—Statistical Art and Science |
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10/12/2019 |
16:20 – 17:20 |
KY_4_(Chair) |
DasGupta, Anirban |
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KY_4_Speaker |
Wolfe,
Patrick |
Statistical Distributions and Network Testing |
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10/11/2019 |
13:20-14:30 |
PL_1_(Chair) |
Cheng, Chin-I |
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PL_1_1 |
Sellers, Kimberly |
Flexible Regression Models for Dispersed
Count Data |
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PL_1_2 |
Gustafson, Paul |
Limiting posterior
distributions from partially identified models: How do they arise, and what
do they tell us? |
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10/12/2019 |
13:20 – 14:30 |
PL_2_(Chair) |
Daniels, John |
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PL_2_1 |
Datta, Susmita |
Advances and
Challenges in Single Cell RNA-Seq Analysis |
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PL_2_2 |
Preisser, John |
Estimating the zero
cell of multivariate binary data from partially-sampled clusters |
General-Invited sessions
(G-x-i: G: General-Invited, x: Session
sequence, i: Speaker Sequence; I = 0:
Session Chair)
All General-Invited sessions
are in Room #515
General-Invited Session: Topics and Session Chairs
Date |
Time |
Room |
Session # |
Gen-Invited Session Chair |
10/11/19 |
09:15 - 10:35 |
515 |
G_1 |
Lazar, Drew |
10/11/19 |
11:00 - 12:20 |
515 |
G_2 |
Pararai, Mavis |
10/12/19 |
09:15 - 10:35 |
515 |
G_5 |
Aljarrah, Mohammad A. |
10/12/19 |
11:00
- 12:20 |
515 |
G_6 |
McTague,
Jaclyn |
10/11/2019 |
Time |
Room |
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G-1-(Chair) |
Lazar, Drew |
9:15 – 10:35 |
Breakout Room 8: Room #515 |
G-1-1 |
Abujarad,
Mohammed H.A. |
Bayesian Survival
Analysis of Topp-Leone Generalized Family with Stan |
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G-1-2 |
Mynbaev,
Kairat |
Nonparametric kernel
estimation of unrestricted distributions |
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G-1-3 |
Louzada-Neto,
Francisco |
Efficient Closed-Form
MAP Estimators for Some Survival Distributions and Their
Applications to Embedded Systems |
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G-2-(Chair) |
Pararai, Mavis |
11:00 – 12:20 |
Breakout Room 8: Room #515 |
G-2-1 |
Ahmed, Bilal Peer |
Inflated Size-Biased
Modified Power Series Distributions and its Applications |
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G-2-2 |
Odhiambo, Collins |
Extended version of
Zero-inflated Negative Binomial Distribution with Application to HIV Exposed
Infant Count Data |
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G-2-3 |
Ogawa, Mitsunori |
Parameter estimation
for discrete exponential families under the presence of nuisance parameters |
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G-2-4 |
Peng, Jie |
Improved Prediction
Intervals for Discrete Distributions |
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10/12/2019 |
Time |
Room |
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G-5-(Chair) |
Aljarrah, Mohammad |
9:15 – 10:35 |
Breakout Room 8:
Room #515 |
G_5_1 |
Feng, Yaqin |
Stability and instability of steady states for a branching
random walk |
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G_5_2 |
Sepanski, Jungsywan |
Constructing Bivariate Copulas with Distributional Distortions |
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G_5_3 |
Lazar, Drew |
Robust and scalable optimization on manifolds |
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G_5_4 |
Smith, Scott |
A Generalization of the Farlie-Gumbel-Morgenstern
and Ali-Mikhail-Haq Copulas |
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G-6-(Chair) |
McTague, Jaclyn |
11:00 – 12:20 |
Breakout Room 8: Room #515 |
G-6-1 |
McTague, Jaclyn |
Repeated Significance
Testing of Normal Variables with Unknown Variance |
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G-6-2 |
Wang, Dongliang |
Empirical likelihood
inference for Kolmogorov-Smirnov test given censored data |
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G-6-3 |
Mesbah, Mounir |
Current statistical
issues in HRQoL research: Testing local
independence in latent variable models |
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G-6-4 |
Bulut,
Murat |
Robust Logistic Regression based on Liu
estimator |
Topic-Invited Sessions (TI-x-i:
TI: Topic-Invited, x: Session
sequence, i: Speaker Sequence)
10/11/2019 |
Time |
Room |
Topic |
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TI_1_(Chair) |
Abdelrazeq, Ibrahim |
9:15 - 10:35 |
Breakout Room 1: Room#215(A,H) |
Goodness-of-Fit-Tests (in stat
modeling) |
TI_01_1 |
Al-Labadi, Luai |
A Bayesian Nonparametric Test for Assessing Multivariate
Normality |
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TI_01_2 |
Oraby,
Tamer |
Modeling Progression of Co-Morbidity Using Bivariate Markov
Chains |
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TI_01_3 |
El Ktaibi, Farid |
Bootstrapping the Empirical Distribution of a Stationary Process
with Change-point |
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TI_01_4 |
Abdelrazeq, Ibrahim |
The Spread Dynamics of S&P 500 vs Levy-Driven OU Processes |
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TI_2_(Chair) |
Al-Aqtash, Raid |
9:15 - 10:35 |
Breakout Room 2: Room #201 |
Generalized distributions and
Applications |
TI_02_1 |
Elkadry,
Alaa |
Analyzing Continuous Randomized Response Data with an
Indifference-Zone Selection Procedure |
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TI_02_2 |
Aljarrah, Mohammad A. |
A new generalized normal regression model. |
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TI_02_3 |
Ahmad, Murad |
On the class of Transmuted-G Distributions |
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TI_02_4 |
Al-Aqtash, Raid |
On the Gumbel-Burr XII Distribution; Regression and Application |
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TI_3_(Chair) |
Alzaghal, Ahmad |
9:15 - 10:35 |
Breakout Room 3: Room #203 |
Distributions and Applications |
TI_03_1 |
Hamed, Duha |
New Families of Generalized Lomax Distributions: Properties and
Applications |
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TI_03_2 |
Hamdan, Hasan (Pre. Abdurasul, Emad) |
Approximating and Characterizing Infinite Scale Mixtures |
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TI_03_3 |
Johnston, Douglas E. |
A Recursive Bayesian Model for the Excess Distribution with
Stochastic Parameters |
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TI_03_4 |
Abdurasul, Emad |
The Product Limit survival function Distribution with Small
Sample Inference |
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TI_4_(Chair) |
Amezziane, Mohamed |
9:15 - 10:35 |
Breakout Room 4: Room #202 |
Models for Complex Data |
TI_04_1 |
Vinogradov, Vladimir (pre. By Yaqin Feng) |
On two extensions of Feller-Spitzer class of Bessel densities |
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TI_04_2 |
Bhattacharjee, Abhishek |
Empirical Bayes Intervals for the Selected Mean |
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TI_04_3 |
Yuanqing
Zhang |
Inference for Partially Linear Additive Higher Order Spatial
Autoregressive Model with Spatial Autoregressive Error and unknown
Heteroskedasticity |
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TI_04_4 |
Nguyen, Yet |
A histogram-Based Method for False Discovery Rate Control in Two
Independent Experiments |
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TI_6_(Chair) |
Arslan, Olcay |
9:15 - 10:35 |
Breakout Room 6: Room #512 |
Some non-normal distributions
and their applications in robust statistical analysis |
TI_6_1 |
Arslan,
Olcay |
Multivariate
Laplace and multivariate skewed Laplace distributions
and their applications in robust
statistical analysis |
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TI_6_2 |
Çelikbıçak, Müge
B. |
Parameter
Estimation in MANOVA with Repeated Non-normal Measures |
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TI_6_3 |
Bulut, Yakup
Murat |
Matrix
variate extensions of symmetric and skew Laplace distributions:
Properties, parameter estimation and
applications |
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TI_6_4 |
Ozdemir, Senay |
Combining
Heavy-Tailed Distributions and Empirical
Likelihood method for Linear Regression Model |
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TI_7_(Chair) |
Brazauskas, Vytaras |
9:15 - 10:35 |
Breakout Room 7: Room #514 |
Actuarial Statistics |
TI_7_1 |
Su, Jianxi |
Full-range
tail dependence copulas for modeling dependent insurance and financial
data |
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TI_7_2 |
Lee, Gee |
General
insurance deductible ratemaking (and extensions) |
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TI_7_3 |
Baron,
Michael |
Sequential
testing and post-analysis of credibility |
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TI_7_4 |
Brazauskas,
Vytaras |
Modeling
severity and measuring tail risk of Norwegian fire claims |
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TI_8_(Chair) |
Chatterjee, Arpita |
11:00 - 12:20 |
Breakout Room 1: Room #215(A,H) |
Statistical Advancements in
Health Sciences |
TI_8_1 |
Ghosh, Santu |
Two-sample
Tests for High Dimensional Means with Prepivoting and Random
Projection |
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TI_8_2 |
Maity, Arnab Kumar |
Bayesian
Data Integration and Variable Selection for Pan-Cancer Survival Prediction
using Protein Expression Data |
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TI_8_3 |
Sen, Ananda |
Honey I
Shrunk the Intercept |
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TI_8_4 |
Chatterjee,
Arpita |
An
Alternative Bayesian Testing to Establish Non-inferiority. |
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TI_9_(Chair) |
Coelho, Carlos Agra |
11:00 - 12:20 |
Breakout Room 2: Room #201 |
Contemporary Methods in
Distribution Theory and Likelihood Inference |
TI_9_1 |
Chen, Ding-Geng |
A
statistical distribution for simultaneously modeling skewness, kurtosis and
bimodality |
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TI_9_2 |
Bekker,
Andriette |
Class of
matrix variate distributions: a flexible approach based on the mean-mixture
of normal model |
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TI_9_3 |
Coelho,
Carlos Agra |
The test
for a two-block circular-spherical covariance structure for samples of random
sizes |
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TI_9_4 |
Babic, Sladana |
Comparison
and classification of flexible distributions for multivariate skew and
heavy-tailed data |
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TI_10_(Chair) |
Cooray, Kahadawala |
11:00 - 12:20 |
Breakout Room 3: Room #203 |
Parametric models for Actuarial
Applications |
TI_10_1 |
Mdziniso,
Nonhle Channon |
Odd Pareto
families of distributions for modeling loss payment data |
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TI_10_2 |
Cheng, Chin-I |
Bayesian estimators of the Odd Weibull distribution with
actuarial application |
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TI_10_3 |
Sepanski
(sub for Samanthi Ranadeera) |
On bivariate distorted copulas |
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TI_11_(Chair) |
Diawara, Norou |
11:00 - 12:20 |
Breakout Room 4: Room #202 |
Statistical Methods for Space
and Time Applications |
TI_11_1 |
Peng,
Stephen |
A Flexible Univariate
Autoregressive Time-Series Model for Dispersed Count Data |
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TI_11_2 |
Hitchcock,
David |
A Spatio-temporal Model Relating Gage Height Data to
Precipitation at South Carolina Locations |
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TI_11_3 |
Fofana, Demba |
Combining
Assumptions and Graphical Network into Gene Expression Data Analysis |
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TI_11_4 |
Chaganty, Rao |
Models for
selecting differentially expressed genes in microarray experiments |
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TI_12_(Chair) |
Flegal, James M. |
11:00 - 12:20 |
Breakout Room 5: Room #510 |
Advances in Bayesian Theory and Computation |
TI_12_1 |
Jones, Galin L. |
Fully
Bayesian Penalized Regression with a Generalized Bridge Prior |
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TI_12_2 |
Womack,
Andrew |
Horseshoes,
Shape Mixing, and Ultra-sparse Locally Adaptive Shrinkage |
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TI_12_3 |
Flegal,
James M. |
Weighted
batch means estimators in Markov chain Monte Carlo |
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TI_12_4 |
Sanz-Alonso,
Daniel |
Scalable
graph-based Bayesian semi-supervised learning |
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TI_13_(Chair) |
George, Olusegun |
11:00 - 12:20 |
Breakout Room 6: Room #512 |
Exchangeability in Statistical
Inference - Theory and Applications |
TI_13_1 |
Zelterman, Dan |
Distributions
for Exchangeable p-Values under an unspecified Alternative
Hypothesis |
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TI_13_2 |
Peng, Hanxiang |
An
Empirical Likelihood Approach of Testing of Multivariate Symmetries |
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TI_13_3 |
Szabo, Aniko |
Semi-parametric
Model for Exchangeable Clustered Binary Outcomes |
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TI_13_4 |
Olufadi, Yunusa |
EM Bayesian
variable selection for clustered discrete and continuous outcomes |
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TI_14_(Chair) |
Ghosh, Indranil |
11:00 - 12:20 |
Breakout Room 7 : Room #514 |
Probability and Statistical
models with applications |
TI_14_1 |
Ghosh, Souparno |
Coherent
Multivariate Feature Selection and Inference across multiple databases |
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TI_14_2 |
Yanev,
George P. |
On Arnold-Villasen ̃or conjectures for characterizing
exponential distribution |
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TI_14_3 |
Alzaatreh,
Ayman |
Truncated
T-X family of distributions |
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TI_14_4 |
Mallick, Avishek |
An Inflated
Geometric Distribution and its application |
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TI_15_(Chair) |
Berrocal, Veronica |
14:45 - 16:05 |
Breakout Room 1: Room #215(A,H) |
Comparing Spatial Fields |
TI_15_1 |
Berrocal, Veronica |
Comparing spatial fields to detect systematic biases in regional
climate models |
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TI_15_2 |
Andrews, Beth |
Partially specified spatial autoregressive model with artificial
neural network |
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TI_15_3 |
Diawara, Norou |
Density Estimation of Spatio-temporal
Point Patterns using Moran's Statistic |
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TI_15_4 |
Daniels, John |
Seeing RED: A New
Statistical Solution to an Old Categorical Data Problem |
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TI_16_(Chair) |
Hannig,
Jan |
14:45 - 16:05 |
Breakout Room 2: Room #201 |
Nonlinear Functionals of Probability Distributions |
TI_16_1 |
Al-Mofleh, Hazem |
Wrapped Circular Statistical Distributions and Applications |
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TI_16_2 |
Xia, Aihua |
Probability Density Quantiles: Their Divergence from or
Convergence to Uniformity |
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TI_16_3 |
Hannig,
Jan |
Model Selection without penalty using Generalized Fiducial
Inference |
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TI_16_4 |
Broniatowski, Michel |
A review on divergence based inference
in parametric and semiparametric models |
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TI_17_(Chair) |
Kao, Ming-Hung (Jason) |
14:45 - 16:05 |
Breakout Room 3: Room #203 |
Design and analysis of complex
experiments: Theory and applications |
TI_17_1 |
Sung, Chih-Li(Charlie) |
Exploiting variance reduction potential in local Gaussian
process search for large computer experiments |
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TI_17_2 |
Phoa,
Frederick |
A systematic construction of cost-efficient designs fororder-of-addition experiments |
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TI_17_3 |
Rha,
Hyungmin |
A probabilistic subset search (PSS) algorithm for optimizing
functional data samplingdesigns |
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TI_17_4 |
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TI_18_(Chair) |
Kozubowski, Tomasz |
14:45 - 16:05 |
Breakout Room 4: Room #202 |
Discrete Stochastic Models and
Applications |
TI_18_1 |
Tomoaki,
Imoto |
Bivariate
GIT distribution |
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TI_18_2 |
Panorska, Anna K. |
Discrete
Pareto Distributions, Butterfly Diet Breadth, and Climate Change |
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TI_18_3 |
Otunuga
, Olusegun |
Closed form probability distribution of number of infections at
a given time in a stochastic SIS epidemic model |
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TI_18_4 |
Schissler, A. Grant |
On
Simulating Ultra High-Dimensional Multivariate Discrete Data |
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TI_19_(Chair) |
Kumar, C. Satheesh |
14:45 - 16:05 |
Breakout Room 5: Room #510 |
Distribution Theory |
TI_19_1 |
Subha, R. Nair |
A
generalization to the log-Weibull distribution and its applications in cancer
research |
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TI_19_2 |
Dharmaja, S.H.S. |
On
logarithmic Kies distribution |
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TI_19_3 |
Chacko,
Manoj |
Bayesian
Analysis of Weibull distribution based on Progressive type-II Censored
Competing Risks Data |
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TI_19_4 |
Kumar, C.
Satheesh |
On a Wide
Class of Discrete Distribution |
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TI_20_(Chair) |
Levine, Michael |
14:45 - 16:05 |
Breakout Room 6: Room #512 |
Recent advances involving latent
variable models for various distributions |
TI_20_1 |
Levine,
Michael |
Estimation
of two-component skew normal mixtures where one component is known |
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TI_20_2 |
Wu, Yichao |
Nonparametric
estimation of multivariate mixtures |
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TI_20_3 |
Davila,
Victor Hugo Lachos |
Finite mixture modeling of censored data using the multivariate
skew-normal distribution |
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TI_20_4 |
Zhang, Lingsong |
On the
analysis of data that lies in the cone |
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TI_21_(Chair) |
Li, Daoji |
14:45 - 16:05 |
Breakout Room 7: Room #514 |
Big Data and
Dimension Reduction |
TI_21_1 |
Wang, Haiying |
Optimal
Subsampling: Sampling with Replacement vs Poisson Sampling |
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TI_21_2 |
Chen, Guangliang |
All data
are "documents": A scalable spectral clustering framework based on
landmark points and cosine similarity |
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TI_21_3 |
Zhang, Teng |
Robust PCA
by Manifold Optimization |
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TI_21_4 |
Li, Daoji |
High-dimensional
interaction detection with false sign rate control |
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TI_22_(Chair) |
Chen, Ding-Geng
|
16:20 - 17:40 |
Breakout Room 1: Room #215(A,H) |
Statistical Modeling for
Degradation Data I |
TI_22_1 |
Ng, Hon
Keung Tony |
Improved
Techniques for Parametric and Nonparametric Evaluations of the First-Passage
Time of Degradation Processes |
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TI_22_2 |
Gao, Yong |
A
Hierarchical Bayesian Bi-exponential Wiener Process for Luminosity
Degradation of Display Products |
||
TI_22_3 |
Lee,
I-Chen |
Global
Planning of Accelerated Degradation Tests |
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TI_22_4 |
Jayalath,
Kalanka |
A Bayesian
Survival Analysis for the Inverse Gaussian Data |
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TI_23_(Chair) |
Piperigou, Violetta |
16:20 - 17:40 |
Breakout Room 2: Room #201 |
Advances in distribution theory
and statistical methodologies |
TI_23_1 |
Oyamakin S. O. |
Some New Nonlinear Growth Models
For Biological Processes based on Hyperbolic Sine
Function |
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TI_23_2 |
Ferreira, Johan |
Alternative Dirichlet priors for
estimation of Shannon entropyusing countably
discrete likelihoods∗ |
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TI_23_3 |
Piperigou, Violetta |
Maximum Likelihood Estimators
for a Class of Bivariate Discrete Distributions |
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TI_23_4 |
||||
TI_24_(Chair) |
McKean, Joseph |
16:20 - 17:40 |
Breakout Room 3: Room #203 |
Big Data: Algorithms,
Methodology, and Applications |
TI_24_1 |
Schafer,
Chad |
Astrostatistics in the Era of LSST |
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TI_24_2 |
Lee, Kevin |
Temporal Exponential-Family
Random Graph Models with Time-Evolving Latent Block Structure for Dynamic
Networks |
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TI_24_3 |
Zeitler,
David |
Rank Based
Estimation With Skew Normal Error
Distributions Using Big Data Sets |
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TI_24_4 |
Kapenga, John |
Computation
of High Dimensions Integrals |
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TI_25_(Chair) |
Melnykov, Volodymyr |
16:20 - 17:40 |
Breakout Room 4: Room #202 |
New developments in finite
mixture modeling with applications |
TI_25_1 |
Melnykov,
Yana |
On finite
mixture modeling of processes with change points |
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TI_25_2 |
Michael, Semhar |
Finite
mixture of regression models for data from complex survey design |
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TI_25_3 |
Sarkar, Shuchismita |
Finite
mixture modeling and model-based clustering for directed weighted networks |
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TI_25_4 |
Melnykov,
Igor |
Positive
and negative equivalence constraints in the semi-supervised K-means algorithm |
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TI_26_(Chair) |
Peng, Jianan |
16:20 - 17:40 |
Breakout Room 5: Room #510 |
Generalized
and Fiducial Inference with Applications |
TI_26_1 |
Weerahandi,
Samaradasa |
Generalized Inference
with Application to Business and Clinical Analytics |
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TI_26_2 |
Krishnamoorthy,
Kalimuthu |
Fiducial
Inference with Applications |
||
TI_26_3 |
Gunasekera, Sumith |
On
Estimating the Reliability in a Multicomponent System based on
Progressively-Censored Data from Chen Distribution |
||
TI_26_4 |
Peng,
Jianan |
Successive Comparisons
for One-way Layout under Heteroscedasticity |
||
TI_27_(Chair) |
Muthukumarana, Saman |
16:20 - 17:40 |
Breakout Room 6: Room #512 |
Bayesian Methods with
Applications |
TI_27_1 |
Bonner,
Simon |
Modelling
Score Based Data from Photo-Identification Studies of Wild Animals |
||
TI_27_2 |
Pratola, Matthew |
Adaptive
Splitting Bayesian Regression Tree Models for Image Analysis |
||
TI_27_3 |
Burkett,
Kelly |
Markov
chain Monte Carlo sampling of gene genealogies conditional on genotype data
from trios |
||
TI_27_4 |
Muthukumarana,
Saman |
Model Based
Estimation of Baseball Batting Metrics |
||
TI_5_(Chair) |
George, Tyler |
16:20
- 17:40 |
Breakout Room 5: Room #514 |
TX Family: Extensions and
Inference |
TI_5_1 |
Almomani, Ayman |
TX: The
Extended Family |
||
TI_5_2 |
Schmegner, Claudia |
TX Family
and Horseshoe Priors |
||
TI_5_3 |
Aldeni,
Mahmoud |
TX Family
and Survival Models |
||
TI_5_4 |
Bahadi, Taoufik
|
TX Family
of Link functions for Binary Regression |
||
10/12/19 |
Time |
Room |
Topic |
|
TI_30_(Chair) |
Oluyede,
Broderick |
9:15 - 10:35 |
Breakout Room 2: Room #201 |
Copulas, Informational
Energy, Exponential Dominance and Uncertainty for Generalized and
Multivariate Distributions |
TI_30_1 |
Oluyede, Broderick |
Informational
Energy, Stochastic Inequalities and Bounds for Weighted Weibull-Type
Distributions. |
||
TI_30_2 |
Makubate,
Boikanyo |
A New
Generalized Weibull Distribution with Applications to Lifetime
Data |
||
TI_30_3 |
Kim, Jong
Min |
Change
point detection method with copula conditional distribution to multistage
sequential control chart. |
||
TI_30_4 |
|
|
||
TI_31_(Chair) |
Omolo,
Bernard |
9:15 - 10:35 |
Breakout Room 3: Room #203 |
Statistical Methods for High‐Dimensional Data Analysis: Application to Genomics |
TI_31_1 |
Omolo,
Bernard |
A Model-based Approach to Genetic Association Testing in Malaria
Studies |
||
TI_31_2 |
Galoppo,
Travis + Kogan, Clark |
A GPU Enhanced Bayesian Ordinal Logistic Regression Model of
Hospital Antimicrobial Usage |
||
TI_31_3 |
Almohalwas,
Akram |
Analysing of
Donald Trump's Twitter Data Using Text Mining and Social Network
Analysis |
||
TI_31_4 |
||||
TI_32_(Chair) |
Ng, Hon Keung Tony |
9:15 - 10:35 |
Breakout Room 4: Room #202 |
Statistical Models and Methods
for Analysis of Reliability and Survival Data |
TI_32_1 |
Chen, Din-Geng |
Homoscedasticity
in the Accelerated Failure Time Model |
||
TI_32_2 |
Ghosh,
Indranil |
Bivariate
Beta and Kumaraswamy Models developed using the Arnold-Ng Bivariate
Beta Distribution |
||
TI_32_3 |
Lio, Yuh Long |
Estimation
of Stress-Strength for Burr XII distribution based on the progressively first
failure-censored samples |
||
TI_32_4 |
Pal, Suvra |
A New
Estimation Algorithm for a Flexible Cure Rate Model |
||
TI_33_(Chair) |
Pigeon, Mathieu |
9:15 - 10:35 |
Breakout Room 5: Room #510 |
Recent developments in predictive
distribution modelling with applications in insurance |
TI_33_1 |
Shi, Peng |
Regression
for Copula-linked Compound Distributions with Applications in Modeling
Aggregate Insurance Claims |
||
TI_33_2 |
Mailhot, Melina |
Multivariate geometric expectiles and
range value-at-risk |
||
TI_33_3 |
Herrmann,
Klaus |
The Extreme
Value Limit Theorem for Dependent Sequences of Random Variables |
||
TI_33_4 |
Duval,
Francis |
Gradient
Boosting-Based Model for Individual Loss Reserving |
||
TI_34_(Chair) |
Provost, Serge |
9:15 - 10:35 |
Breakout Room 6: Room #512 |
Recent Distributional Advances
Involving Population and Sample Moments |
TI_34_1 |
Provost,
Serge |
On
recovering sample points from their associated moments and certain
moment-based density estimation methodologies |
||
TI_34_2 |
Nkurunziza,
Sévérien |
Some
identities for the risk and bias of shrinkage-type estimators in elliptically
contoured distributions |
||
TI_34_3 |
Mohsenipour, Akbar |
Approximating
the distribution
of various types of quadratic expressions on
the basis of their moments |
||
TI_34_4 |
Kang, Sang (John) |
Moment-based density approximation techniques
as applied to heavy-tailed distributions |
||
TI_35_(Chair) |
Qingcong
Yuan (Org -Qian, Lianfen_ |
9:15 - 10:35 |
Breakout Room 7: Room #514 |
Recent Advances in Analyzing
Medical Data and Dimension Reduction |
TI_35_1 |
Yin,
Xiangrong |
Moment
Kernel for Estimating Central Mean Subspace and Central Subspace |
||
TI_35_2 |
Zhang, Wei |
Imputation
of Missing Data in the State Inpatient Databases |
||
TI_35_3 |
He, Wenqing |
Perturbed Variance
Based Null Hypothesis Tests with An Application to Clayton Models |
||
TI_35_4 |
Qian,
Lianfen |
Recent
analysis of semi-competing risks data |
||
TI_36_(Chair) |
Richter, Wolf-Dieter |
11:00 - 12:20 |
Breakout Room 1: Room #215(A,H) |
Multivariate distributions |
TI_36_1 |
Takemura, Akimichi |
Holonomic gradient method for evaluation of multivariate probabilities |
||
TI_36_2 |
Nolan, John |
Multivariate
Generalized Logistic Laws |
||
TI_36_3 |
Kozubowski,
Tomasz |
Multivariate
models connected with random sums and maxima of dependent Pareto
components |
||
TI_36_4 |
Richter,
Wolf-Dieter |
On (p_1,...,p_k)-spherical
distributions |
||
TI_37_(Chair) |
Sarhan, Ammar |
11:00 - 12:20 |
Breakout Room 2: Room #201 |
Generalization of lifetime
distributions |
TI_37_1 |
Sarhan, Ammar |
A new extension of the two-parameter bathtub hazard shaped
distribution |
||
TI_37_2 |
Alzaghal, Ahmad |
A Generalized Family of Lindley Distribution: Properties and
Applications |
||
TI_37_3 |
Pararai,
Mavis |
The Weibull Linear Failure Rate Distribution and Its
Applications |
||
TI_37_4 |
Sinha,
Sanjoy K. |
Joint
modeling of longitudinal and time-to-event data with covariates subject to
detection limits |
||
TI_38_(Chair) |
Peng, Hanxiang |
11:00 - 12:20 |
Breakout Room 3: Room #203 |
Empirical Likelihood |
TI_38_1 |
Peng, Hanxiang |
Maximum
empirical likelihood estimation in U-statistics based general estimating
equations. |
||
TI_38_2 |
Yu, Jihnhee |
Bayesian
empirical likelihood approach to compare quantiles |
||
TI_38_3 |
Zhong,
Ping-Shou |
Order-restricted
inference for means with missing values |
||
TI_38_4 |
Xie, Yanmei |
Analysis of
nonignorable missingness in risk factors for hypertension |
||
TI_40_(Chair) |
Wang, Shan |
11:00 - 12:20 |
Breakout Room 5: Room #510 |
Recent Development in
Nonparametric and Semiparametric Techniques |
TI_40_1 |
Wang, Shan |
Estimation of SEM with MELE approach |
||
TI_40_2 |
Liu, Ruiqi |
Optimal Nonparametric Inference via Deep Neural Network |
||
TI_40_3 |
Zhao, Wei |
Optimal Sampling Distributions for Generalized Linear Models |
||
TI_40_4 |
Torkashvand,
Elaheh |
Spatial Dynamical Autocorrelation of fMRI
Images |
||
TI_41_(Chair) |
Wang, Xia |
11:00 - 12:20 |
Breakout Room 6: Room #512 |
Bayesian Modeling of Dependent
Non-Gaussian Data |
TI_41_1 |
Chang, Won |
Ice Model
Calibration using Semi-continuous Spatial Data |
||
TI_41_2 |
Pal, Subhadip |
A Bayesian
Framework for Modeling Data on the Stiefel Manifold. |
||
TI_41_3 |
Hu, Guanyu |
A Bayesian
Joint Model of Marker and Intensity of Marked Spatial Point Processes
with Application to Basketball Shot Chart |
||
TI_41_4 |
Wang, Xia |
Power Link
Functions in Ordinal Regression Models with Gaussian Process Priors |
||
TI_42_(Chair) |
Xu, Xiaojian |
11:00 - 12:20 |
Breakout Room 7: Room #514 |
Optimal design, active learning,
and efficient statistics for big data |
TI_42_1 |
Mandal,
Saumen |
Constrained
optimal designs for estimating probabilities in contingency tables |
||
TI_42_2 |
Shay, Garrett Charlie |
Probabilistic and non-probabilistic methods of active learning
for classification |
||
TI_42_3 |
Zheng, Wei |
Incomplete
U-statistic based on division and orthogonal array |
||
TI_42_4 |
Xu,
Xiaojian |
Robust
active learning for approximate linear models |
||
TI_43_(Chair) |
Yin, Xiangrong |
14:45 - 16:05 |
Breakout Room 1: Room #215(A,H) |
Variable selection and dimension
reduction for high-dimension data problems |
TI_43_1 |
Dong, Yuexiao |
On dual
model-free variable selection with two groups of variables |
||
TI_43_2 |
Shao, Xiaofeng |
Inference
for change points in high dimensional data |
||
TI_43_3 |
Wu, Wenbo |
Simultaneous
estimation for semi-parametric multi-index models |
||
TI_43_4 |
Sriperumbudur,
Bharath |
Approximate Kernel PCA: Computational
vs. Statistical Trade-off |
||
TI_44_(Chair) |
Zhang, Jing |
14:45 - 16:05 |
Breakout Room 2: Room #201 |
New Explorations for High-Dimensional Big Data Analysis |
TI_44_1 |
Yuan, Qingcong |
A two-stage
variable selection approach in the analysis of metabolomics and microbiome
data |
||
TI_44_2 |
Zhang, Jing |
A “Split
and Resample” Approach in Big Data Analysis |
||
TI_44_3 |
Shahzad, Mirza Naveed |
Singh-Maddala Distribution: A new
candidate to analyze the extreme value data by linear moment estimation |
||
TI_44_4 |
Fisher,
Thomas |
A split
and merge strategy to variable selection |
||
TI_45_(Chair) |
Zitikis,
Ricardas |
14:45 - 16:05 |
Breakout Room 3: Room #203 |
Risk Measures: Theory,
Inference, and Applications |
TI_45_1 |
Sun, Ning |
The Pareto Optimal Design for Earthquake Index-based Insurance
Based on Exponential Utilities |
||
TI_45_2 |
Wu, Jiang |
A Financial Contagion Measure Based on the Maximal Tail
Dependence Coefficient for Financial Time Series |
||
TI_45_3 |
Zitikis,
Ricardas |
Gini Shortfall: A Coherent Risk Measure |
||
TI_45_4 |
Samanthi, Ranadeera |
Methods for Generating Coherent Distortion Risk Measures |
||
TI_46_(Chair) |
Lio, Yuhlong |
14:45 - 16:05 |
Breakout Room 4: Room #202 |
Statistical Modeling for
Degradation Data II |
TI_46_1 |
Bandyopadhyay, Tathagata |
Inference problems in binary regression model with misclassified
responses |
||
TI_46_2 |
Wang,
Yueyao |
Building
Degradation Index Using Multivariate Sensory Data with Variable Selection |
||
TI_46_3 |
MeInykov,
Volodymyr |
On Model-Based Clustering of Time-Dependent Categorical
Sequences |
||
TI_46_4 |
Davies,
Katherine |
Progressively
Type-II Censored Competing Risks Data from the Linear Exponential
Distribution |
||
TI_47_(Chair) |
Akinsete,
Alfred |
14:45 - 16:05 |
Breakout Room 5: Room #510 |
A new class of generalized
distributions |
TI_47_1 |
Aryal, Gokarna |
Transmuted-G
Poisson Family |
||
TI_47_2 |
Long,
Hongwei |
The Beta
Transmuted Pareto Distribution: Theory and Applications |
||
TI_47_3 |
Chhetri,
Sher B. |
On
the Beta-G Poisson Family |
||
TI_47_4 |
Pokhrel, Keshav P. |
Reliability
Models Using the Composite Generalizers of Weibull Distribution |
||
TI_48_(Chair) |
Huang,
Hsin-Hsiung |
14:45 - 16:05 |
Breakout Room 6: Room #512 |
Statistical
Methodology For Big Data |
TI_48_1 |
Huang, Hsin-Hsiung |
A new statistical strategy for predicting major depressive
disorder using whole-exome genotyping data |
||
TI_48_2 |
Li, Keren |
Score-Matching Representative Approach for Big Data Analysis
with Generalized Linear Models |
||
TI_48_3 |
Xu, Mengyu |
Simultaneous Prediction intervals for high-dimensional Vector
Autoregressive model |
||
TI_48_4 |
Aburweis,
Mohamed |
Comparative study of the distribution of repetitive DNA in model
organisms abstract |
||
TI_28_(Chair) |
Nayak, Tapan |
14:45 - 16:05 |
Breakout Room 7: Room #514 |
Protection of Respondents'
Privacy and Data Confidentiality |
TI_28_1 |
Zhang, Linjun |
The Cost of Privacy: Optimal Rates of Convergence for Parameter
Estimation with Differential Privacy |
||
TI_28_2 |
Sarathy,
Rathindra |
Statistical
Basis for Data Privacy and Confidentiality |
||
TI_28_3 |
Zhang, Cheng |
Novel Post-randomization Methods for Controlling Identity Disclosure
and Preserving Data Utility |
||
TI_28_4 |
Nayak, Tapan |
Discussion |
Student
Posters (5:40 -6:30 pm on 10/11/2019)
All posters
will be presented on the hallway, 2nd floor of the Conference Center
|
Last
Name |
First
Name |
Title |
1 |
Amponsah |
Charles |
A
Bivariate Gamma Mixture Discrete Pareto Distribution |
2 |
Ash |
Jeremy |
Confidence
band estimation methods for accumulation curves at extremely small fractions
with applications to drug discovery |
3 |
Cho |
Min
Ho |
Aggregated
Pairwise Classification of Statistical Shapes |
4 |
Damarjian |
Hanna |
On
the Transmuted Exponential Pareto Distribution |
5 |
Das |
Manjari |
Efficient
nonparametric estimation of population size from incomplete lists |
6 |
Farazi |
Md
Manzur Rahman |
Feature
Selection for a Predictive Model using Machine Learning Techniques on
Mosquito’s Spectral Data |
7 |
George |
Tyler |
Lack-of-fit
Testing Without Replicates Available |
8 |
Goward |
Kenneth |
A
New Generalized Inverse Gaussian Distribution with Bayesian Estimators |
9 |
Ihtisham |
Shumaila |
Alpha
Power Inverse Pareto Distribution and its Properties |
10 |
Ijaz |
Muhammad |
A
New Family of Distributions with Applications |
11 |
Lee |
Joo
Chul |
Online
Updating Method to Correct for Measurement Error in Big Data Streams |
12 |
Lun |
Zhixin |
Simulating
from Skewed Multivariate Distributions: The Cases of Lomax, Mardia’s Pareto
(Type 1), Logistic, Burr and F Distributions |
13 |
Matuk |
James |
Function
Estimation through Phase and Amplitude Separation |
14 |
Maxwell |
Obubu |
The
Kumaraswamy Inverse Lomax Distribution (K-IL): Properties and Applications |
15 |
May |
Paul |
Multiresolution
Techniques for High Precision Agriculture |
16 |
Melchert |
Bryan |
Forecasting
Migration Timing of Sockeye Salmon to Bristol Bay, AK |
17 |
Mohammed
|
Mohanad |
Using
stacking ensemble for microarray-based cancer classification |
18 |
Saha |
Dheeman |
Sparse
Bayesian Envelope |
19 |
Shen |
Luyi |
Bayesian
community detection for weighted sparse networks using mixture of SBM model |
29 |
Shubhadeep |
Chakraborty |
A
New Framework for Distance and Kernel-based Metrics in High Dimensions |
21 |
Ordoñez |
José Alejandro |
Objective
Bayesian Analysis for the Spatial Student t Regression model |
22 |
Soale |
Abdul-Nasah |
On
expectile-assisted inverse regression estimation for sufficient dimension
reduction |
23 |
Wang |
Yang |
On
variable selection in matrix mixture modeling |
24 |
Wang |
Runmin |
Self-Normalization
for High Dimensional Time Series |
25 |
Xing |
Lin |
A metric geometry approach to the weight prediction
problem |
26 |
Yao |
Yaqiong |
Optimal
two-stage adaptive subsampling design for softmax regression |
27 |
Yuu |
Elizabeth |
Quantifying
microbial dark matter using generalized linear models and its impact on
metagenome analyses |
28 |
Zang |
Xiao |
Clustering
Functional Data using Fisher-Rao Metric |
29 |
Zhang |
Han |
Aggregate
Estimation in Sufficient Dimension Reduction for Binary Responses |
30 |
Zhang |
Yangfan |
High
Dimensional Regression Change Point Detection |
31 |
Zhang |
Yingying |
On
model-based clustering of time-dependent categorical sequences |
32 |
Zhu |
Changbo |
Interpoint
Distance Based Two Sample Tests in High Dimension |
33 |
Galarza |
Christian |
On
moments of folded and truncated multivariate extended skew-normal
distributions |
34 |
Yang |
Tiantian |
A Comparison of Several Missing Data Imputation
Techniques for Analyzing Different Types of Missingness |