Nicole Lazar

Professor of Statistics

Nicole Lazar

Huck Affiliations

Links

Publication Tags

Empirical Likelihood Magnetic Resonance Imaging Statistics Cluster Analysis Correlation Structure Generalized Estimating Equations Interaction Persistence Data Analysis Likelihood Smoothing Brain Functional Neuroimaging Regression Inference Quantile Meta Analysis Review Stroke Point Cloud Bayes Factor Episodic Memory Practice (Psychology) Ridge Regression Lack

Most Recent Papers

The neuroscience of human connection and leadership

Nicole Lazar,

An integrative multivariate approach for predicting functional recovery using magnetic resonance imaging parameters in a translational pig ischemic stroke model

Erin Kaiser, J. Poythress, Kelly Scheulin, Brian Jurgielewicz, Nicole Lazar, Cheolwoo Park, Steven Stice, Jeongyoun Ahn, Franklin West, 2021, Neural Regeneration Research on p. 842-850

A review of empirical likelihood

Nicole A. Lazar, 2021, Annual Review of Statistics and Its Application on p. 329-344

Split sample empirical likelihood

Adam Jaeger, Nicole A. Lazar, 2020, Computational Statistics and Data Analysis

Data, data, everywhere...

Nicole Lazar, 2020, Harvard Data Science Review

Bayesian empirical likelihood for ridge and lasso regressions

Adel Bedoui, Nicole A. Lazar, 2020, Computational Statistics and Data Analysis

Moving to a World Beyond “p < 0.05”

Ronald L. Wasserstein, Allen L. Schirm, Nicole A. Lazar, 2019, American Statistician on p. 1-19

Persistence Terrace for Topological Inference of Point Cloud Data

Chul Moon, Noah Giansiracusa, Nicole A. Lazar, 2018, Journal of Computational and Graphical Statistics on p. 576-586

Introduction

Hadley Wickham, Jennifer Bryan, Nicole Lazar, 2018, American Statistician on p. 1

Bayesian empirical likelihood methods for quantile comparisons

Albert Vexler, Jihnhee Yu, Nicole Lazar, 2017, Journal of the Korean Statistical Society on p. 518-538

Most-Cited Papers

The ASA's Statement on p-Values

Ronald L. Wasserstein, Nicole A. Lazar, 2016, American Statistician on p. 129-133

Moving to a World Beyond “p < 0.05”

Ronald L. Wasserstein, Allen L. Schirm, Nicole A. Lazar, 2019, American Statistician on p. 1-19

A Meta-Analysis of fMRI Activation Differences during Episodic Memory in Alzheimer's Disease and Mild Cognitive Impairment

Douglas P. Terry, Dean Sabatinelli, A. Nicolas Puente, Nicole A. Lazar, L. Stephen Miller, 2015, Journal of Neuroimaging on p. 849-860

Selection of working correlation structure in generalized estimating equations via empirical likelihood

Jien Chen, Nicole A. Lazar, 2012, Journal of Computational and Graphical Statistics on p. 18-41

A capstone course for undergraduate statistics majors

Nicole A. Lazar, Jaxk Reeves, Christine Franklin, 2011, American Statistician on p. 183-189

Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging

D. Andrew Brown, Nicole A. Lazar, Gauri S. Datta, Woncheol Jang, Jennifer E. McDowell, 2014, NeuroImage on p. 97-112

Nonparametric variogram modeling with hole effect structure in analyzing the spatial characteristics of fMRI data

Jun Ye, Nicole A. Lazar, Yehua Li, 2015, Journal of Neuroscience Methods on p. 101-115

Sparse geostatistical analysis in clustering fMRI time series

Jun Ye, Nicole A. Lazar, Yehua Li, 2011, Journal of Neuroscience Methods on p. 336-345

Volubility of the human infant

Suneeti Nathani Iyer, Hailey Denson, Nicole Lazar, D. Kimbrough Oller, 2016, Clinical Linguistics and Phonetics on p. 470-488

Practice-related changes in neural activation patterns investigated via wavelet-based clustering analysis

Jinae Lee, Cheolwoo Park, Kara A. Dyckman, Nicole A. Lazar, Benjamin P. Austin, Qingyang Li, Jennifer E. Mcdowell, 2013, Human Brain Mapping on p. 2276-2291