Nicole Lazar

Professor of Statistics

Nicole Lazar

Huck Affiliations

Links

Publication Tags

Empirical Likelihood Magnetic Resonance Imaging Regression Brain Invariance Monte Carlo Simulation Generalized Estimating Equations Exact Test Lasso Ridge Regression Persistence Correlation Structure Critical Value Interaction Likelihood Smoothing Metropolis Hastings Estimate Model Misspecification Ridge Functional Neuroimaging Review Infant Swine Inference

Most Recent Papers

A group comparison in fMRI data using a semiparametric model under shape invariance

Arunava Samaddar, Brooke S. Jackson, Christopher J. Helms, Nicole A. Lazar, Jennifer E. McDowell, Cheolwoo Park, 2022, Computational Statistics and Data Analysis

A group comparison in fMRI data using a semiparametric model under shape invariance

A. Samaddar, B. Jackson, C. Helms, Nicole Lazar, J. McDowell, C. Park, 2021, Computational Statistics and Data Analysis

The neuroscience of human connection and leadership

Nicole Lazar, 2021,

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

Bayesian empirical likelihood for ridge and lasso regressions

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

Data, data, everywhere...

Nicole Lazar, 2020, Harvard Data Science Review

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

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

Volubility of the human infant

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

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

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

Computing critical values of exact tests by incorporating monte carlo simulations combined with statistical tables

Albert Vexler, Young Min Kim, Jihnhee Yu, Nicole A. Lazar, Alan D. Hutson, 2014, Scandinavian Journal of Statistics on p. 1013-1030

Bayesian empirical likelihood for ridge and lasso regressions

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