Keynote Speaker

• Dr. Trivellore Raghunathan, University of Michigan - Multiple Uses of Multiple Imputation

• Multiple imputation is becoming increasingly popular approach for analyzing incomplete data. Many methods for handling complex data structures, differing types of variables have been developed and implemented in various statistical packages. Many other problems in statistics can be formulated as a missing data problem where the multiple imputation paradigm may be used. These include causal inference, inference from mixed mode surveys, Bayesian inference, disclosure limitation, measurement error, outlier detection etc. This talk will briefly review the multiple imputation approach for missing values and then extend its use to aforementioned problems.

Posters

MSU Graduate Students

• Danielle Barnes, Statistics and Probability - Segregating Pathways of Phenotypic Expression in Breast Cancer Models
• Abhishek Kaul, Statistics and Probability - Properties of Regularized Estimators for Long Range Dependent High Dimensional Data
• Lei Liu - Computer Science - Recursive NMF: Efficient Label Tree Learning for Large Multi-Class Problems
• Ashwini Maurya, Statistics and Probability - A Joint Convex Penalty for Inverse Covariance Matrix Estimation
• Robert McCann, Entomology - Effects of Larval Habitat Density and ITN/LLIN Use on the Spatial Distribution of Malaria Vectors
• Siddhartha Nandy, Statistics and Probability - A Variable Selection Technique for Detecting Climate Change Attribution
• Nicholas Panchy, Genetics - Cyclical Gene Expression in Chlamydomonas Reinhardti Shows Conservation From an Ancestral State and Enrichment for Functional Activity with Respect to Phase
• Pablo Reeb - Fisheries and Wildlife - Plasmode Datasets to Validate Statistical Analysis Methods for RNA Sequencing Experiments
• Arora Sunpreet, Computer Science - Latent Fingerprint Matching: Role of Feedback via Exemplar Prints
• Irena Tesnjak, Statistics and Probability - Symptom Management and Interruptions in Cancer Treatment
• Honglang Wang, Statistics and Probability - Empirical Likelihood Inference on Gene-Environment Interactions
• Lei Wang, Animal Science - Model marker effect as correlated random effect in genomic studies
• Changshuai Wei, Epidemiology - Detecting Genetic Heterogeneity in Complex Diseases with a Weighted U Statistic
• Jiahui Zhang, Counseling, Educational Psychology, and Special Education - Facilitating Standard Setting with Diagnostic Classification Models and ROC Analysis

MSU Undergraduate Students

• Jue Wang, Statistics and Probability - Development of a New Calculus and Differential Equations Sequence for Undergraduate Life Sciences Majors

MSU Faculty

• Ian Dworkin, Zoology - Statistical Methods to Test for Genetic Constraints in Drosophila Melanogaster
• Dan Hayes, Fisheries and Wildlife - Modeling Salmon Spawn Timing using Dynamic Modeling and Statistical Fitting
• Hyokyoung (Grace) Hong, Statistics and Probability - Development of a Clinical Prediction Model for an Ordinal Response: A Quantile Approach
• Marianne Huebner, Statistics and Probability - Enhanced Recovery Pathway in Colorectal Surgery
• Qing Lu, Epidemiology - A Weighted U Statistic for Genetic Association Analyses of Sequencing Data
• Luda Sakhanenko, Statistics and Probability - Statistical Estimation of Integral Curves from Diffusion Tensor Imaging Data