Matthew Reimherr
Associate Professor of Statistics
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411 Thomas Bldg
University Park, PA - mlr36@psu.edu
- 814-865-2544
Huck Affiliations
Publication Tags
Testing Phenotype Methodology Longitudinal Data Monte Carlo Simulation Time Series Asthma Principal Components Bioinformatics Screening Genotype Functional Data Analysis Simulation Study Test Statistic Asymptotic Distribution Project Management Genomics Variance Components Autoregressive Model Computational Biology Separability Heritability Genome Functional Model FrameworkMost Recent Papers
Discussion of A General Framework for Regression Modelling
Piotr Kokoszka, Matthew Reimherr, Statistical Modelling on p. 45-49
Types of adult attention-deficit/hyperactivity disorder
Frederick W. Reimherr, Michael Roesler, Barrie K. Marchant, Thomas E. Gift, Wolfgang Retz, Florence Philipp-Wiegmann, Matthew L. Reimherr, 2020, Journal of Clinical Psychiatry
Feature selection for generalized varying coefficient mixed-effect models with application to obesity gwas
Wanghuan Chu, Runze Li, Jingyuan Liu, Matthew Reimherr, 2020, Annals of Applied Statistics on p. 276-298
Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists
John Dziak, Donna Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya Shiyko, 2019, Statistics Surveys
Functional data analysis for computational biology
Marzia Cremona, Hongyan Xu, Kateryna Dmytrivna Makova, Matthew Logan Reimherr, Francesca Chiaromonte, Pedro Madrigal, 2019, Bioinformatics on p. 3211-3213
Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data
John J. Dziak, Donna L. Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P. Shiyko, 2019, Statistics Surveys on p. 150-180
Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists
John Dziak, D Coffman, Matthew Reimherr, J Petrovich, Runze Li, S Shiffman, 2019, Statistical Survey on p. 150 -180
Some recent developments in inference for geostatistical functional data
Piotr Kokoszka, Matthew Reimherr, 2019, Colombian Journal of Statistics
Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists
John Dziak, Donna Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya Shiyko, 2019, Statistics Surveys on p. 150-180
Child Weight Gain Trajectories Linked To Oral Microbiota Composition
Sarah Craig, Daniel Blankenberg, Alice Carla Luisa Parodi, Ian Paul, Leann L. Birch, Jennifer Savage Williams, Michele E. Marini, Jennifer L. Stokes, Anton Nekrutenko, Matthew Logan Reimherr, Francesca Chiaromonte, Kateryna Dmytrivna Makova, 2018, Scientific reports
Most-Cited Papers
Asymptotic normality of the principal components of functional time series
Piotr Kokoszka, Matthew Reimherr, 2013, Stochastic Processes and their Applications on p. 1546-1562
Determining the order of the functional autoregressive model
Piotr Kokoszka, Matthew Logan Reimherr, 2013, Journal of Time Series Analysis on p. 116-129
A functional data analysis approach for genetic association studies
Matthew Reimherr, Dan Nicolae, 2014, Annals of Applied Statistics on p. 406-429
On quantifying dependence
Matthew Reimherr, Dan L. Nicolae, 2013, Statistical Science on p. 116-130
Predictability of shapes of intraday price curves
Piotr Kokoszka, Matthew Reimherr, 2013, Econometrics Journal on p. 285-308
Testing separability of space-time functional processes
P. Constantinou, P. Kokoszka, M. Reimherr, 2017, Biometrika on p. 425-437
Coherent synthesis of genomic associations with phenotypes and home environments
Jesse R. Lasky, Brenna R. Forester, Matthew Logan Reimherr, 2018, Molecular Ecology Resources on p. 91-106
Feature screening for time-varying coefficient models with ultrahigh-dimensional longitudinal data
Wanghuan Chu, Runze Li, Matthew Reimherr, 2016, Annals of Applied Statistics on p. 596-617
Detection of change in the spatiotemporal mean function
Oleksandr Gromenko, Piotr Kokoszka, Matthew Reimherr, 2017, Journal of the Royal Statistical Society. Series B: Statistical Methodology on p. 29-50
Estimating Variance Components in Functional Linear Models With Applications to Genetic Heritability
Matthew Reimherr, Dan Nicolae, 2016, Journal of the American Statistical Association on p. 407-422
News Articles Featuring Matthew Reimherr
Sep 21, 2018
Mouth bacteria in toddlers may predict obesity, study says
Bacteria in a toddler's mouth might help predict later obesity, new research suggests. Scientists at Penn State University found the composition of microorganisms in the mouths of 2-year-olds offers clues to the child's future weight.
Full Article
Sep 20, 2018
Swabbing a child’s mouth for bacteria could predict how likely they are to become obese
A swab of a toddler’s mouth may predict their odds of growing into obese children, a new study suggests. Scientists at Pennsylvania State University discovered that the harmless microorganisms living in a two-year-old’s mouth were less diverse if they had gained more weight more quickly than most since birth.
Full Article
Sep 19, 2018
Child Weight Gain Trajectories Linked To Oral Microbiota Composition
Gut and oral microbiota perturbations have been observed in obese adults and adolescents; less is known about their influence on weight gain in young children.
Full Article