B. Lindsay

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Mixture models : theory, geometry, and applications

B. Lindsay, 1995 .

Improving generalised estimating equations using quadratic inference functions

B. Lindsay, A. Qu, Bing Li, 2000 .

Using Empirical Partially Bayes Inference for Increased Efficiency

B. Lindsay, 1985 .

Minimum disparity estimation for continuous models: Efficiency, distributions and robustness

B. Lindsay, A. Basu, 1994 .

Efficiency versus robustness : the case for minimum Hellinger distance and related methods

B. Lindsay, 1994 .

Moment Matrices: Applications in Mixtures

B. Lindsay, 1989 .

Multivariate Normal Mixtures: A Fast Consistent Method of Moments

B. Lindsay, P. Basak, 1993 .

The Geometry of Mixture Likelihoods: A General Theory

B. Lindsay, 1983 .

Monotonicity of quadratic-approximation algorithms

B. Lindsay, D. Böhning, 1988 .

Semiparametric Estimation in the Rasch Model and Related Exponential Response Models, Including a Simple Latent Class Model for Item Analysis

B. Lindsay, C. Clogg, J. Grego, 1991 .

A Semiparametric Mixture Approach to Case-Control Studies with Errors in Covariables

K. Roeder, R. Carroll, B. Lindsay, 1996 .

Geometrical Foundations of Asymptotic Inference

R. Kass, P. Vos, B. Lindsay, 1997 .

Testing for the number of components in a mixture of normal distributions using moment estimators

B. Lindsay, W. D. Furman, W. Furman, 1994 .

Residual diagnostics for mixture models

K. Roeder, B. Lindsay, 1992 .

Conditional score functions: Some optimality results

B. Lindsay, 1982 .

A UNIVERSALLY CONSISTENT MODIFICATION OF MAXIMUM LIKELIHOOD

B. Lindsay, Byungtae Seo, 2013 .

The Geometry of Mixture Likelihoods, Part II: The Exponential Family

B. Lindsay, 1983 .

A New Index of Fit Based on Mixture Methods for the Analysis of Contingency Tables

B. Lindsay, C. Clogg, T. Rudas, 1994 .

Building and using semiparametric tolerance regions for parametric multinomial models

B. Lindsay, Jiawei Liu, 2009, 0909.0608.

Model Assessment Tools for a Model False World

B. Lindsay, Jiawei Liu, 2009, 1010.0304.

ISSUES AND STRATEGIES IN THE SELECTION OF COMPOSITE LIKELIHOODS

Bruce G. Lindsay, Grace Y. Yi, Jianping Sun, 2011 .

Building adaptive estimating equations when inverse of covariance estimation is difficult

Bruce G. Lindsay, Annie Qu, B. Lindsay, 2003 .

Projected score methods for approximating conditional scores

Bruce G. Lindsay, Richard P. Waterman, B. Lindsay, 1996 .

Weighted likelihood estimating equations: The discrete case with applications to logistic regression

B. Lindsay, M. Markatou, A. Basu, 1997 .

Properties of the Maximum Likelihood Estimator of a Mixing Distribution

Bruce G. Lindsay, B. Lindsay, 1981 .

Model selection in high dimensions: a quadratic‐risk‐based approach

Surajit Ray, Bruce G. Lindsay, B. Lindsay, 2006, math/0611544.

Convergence of the EM algorithm for continuous mixing distributions

Bruce G. Lindsay, B. Lindsay, Yeojin Chung, 2015 .

Model Building for Semiparametric Mixtures

B. Lindsay, F. Bartolucci, R. Pilla, 2006, math/0606077.

A Note on Calculating the π* Index of Fit for the Analysis of Contingency Tables

B. Lindsay, Liwen Xi, 1996 .

The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family

B. Lindsay, D. Böhning, P. Schlattmann, 1994 .

A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation

B. Lindsay, Ji-ping Wang, 2005 .

Measuring the relative effectiveness of moment estimators as starting values in maximizing likelihoods

B. Lindsay, W. D. Furman, 1994 .

Fisher information matrix: A tool for dimension reduction, projection pursuit, independent component analysis, and more

B. Lindsay, W. Yao, 2012 .

A Unified Treatment of Integer Parameter Models

K. Roeder, B. Lindsay, 1987 .

Moments Determine the Tail of a Distribution (But Not Much Else)

B. Lindsay, P. Basak, 2000 .

On the Determinants of Moment Matrices

B. Lindsay, 1989 .

Estimating the number of classes

B. Lindsay, C. Mao, 2007, 0708.2153.

The Graduate School Mining Texts and Social Users Using Time Series and Latent Topics a Dissertation in Information Sciences and Technology

B. Lindsay, P. Mitra, Dongwon Lee, 2022 .

On second-order optimality of the observed Fisher information

B. Lindsay, Bing Li, Bing Li, 1997 .

Nearly universal consistency of maximum likelihood in discrete models

Bruce G. Lindsay, Byungtae Seo, B. Lindsay, 2013 .

A review of semiparametric mixture models

Bruce G. Lindsay, Mary Lesperance, B. Lindsay, 1995 .

B. Lindsay, 2022 .

The topography of multivariate normal mixtures

Surajit Ray, Bruce G. Lindsay, B. Lindsay, 2005 .

Robust Phylogenetic Tree Selection Based on Assessment of Model Building Errors

B. Lindsay, Jiawei Liu, 2003 .

A CLASSICAL INVARIANCE APPROACH TO THE NORMAL MIXTURE PROBLEM

D. Hunter, B. Lindsay, M. Ranalli, 2020 .

An exponential partial prior for improving nonparametric maximum likelihood estimation in mixture models

Bruce G. Lindsay, B. Lindsay, Ji-ping Wang, 2008 .

DIAGNOSTICS FOR THE HOMOGENEITY OF INCLUSION PROBABILITIES IN A BERNOULLI CENSUS

B. Lindsay, C. Mao, 2002 .

A Poisson model for the coverage problem with a genomic application

Bruce G. Lindsay, Chang Xuan Mao, B. Lindsay, 2002 .

Variance estimation of a general u-statistic with appllication to cross-validation

Bruce G. Lindsay, Qing Wang, B. Lindsay, 2014 .

Composite likelihood inference for a class of latent variable models for two factor clustering

B. Lindsay, F. Bartolucci, F. Chiaromonte, 2013 .

A fast score test for generalized mixture models

R. Carroll, B. Lindsay, Y. Ning, 2019, Biometrics.

USING CONFIDENCE DISTRIBUTION SAMPLING TO VISUALIZE CONFIDENCE SETS

B. Lindsay, Daeyoung Kim, 2011 .

THE TOPOGRAPHY OF MULTIVARIATE NORMAL MIXTURES 1 By Surajit Ray and

B. Lindsay, 2020 .

Empirical identifiability in finite mixture models

B. Lindsay, Daeyoung Kim, 2015 .

An Expansive View of EM Algorithms

Bruce G. Lindsay, Prabhani Kuruppumullage Don, David R. Hunter, 2019, Handbook of Mixture Analysis.

The Pennsylvania State University The Graduate School Eberly College of Science AN EM BASED TAGGING SNP SELECTION ALGORITHM INCORPORATING GENOTYPING ERRORS A Thesis in Statistics

B. Lindsay, A. Slavkovic, V. Chinchilli, 2014 .

Model Building for Semiparametric Mixtures

Ramani S. Pilla, B. Lindsay, F. Bartolucci, 2006, math/0606077.

The topography of multivariate normal mixtures

B. Lindsay, Surajit Ray, 2005, math/0602238.

Empirical identifiability in finite mixture models

B. Lindsay, Daeyoung Kim, 2014, Annals of the Institute of Statistical Mathematics.

Computer-assisted analysis of mixtures (C.A.MAN) statistical algorithms

Bruce G. Lindsay, Dankmar Böhning, Peter Schlattmann, 1992 .

Alternative EM methods for nonparametric finite mixture models

B. Lindsay, R. Pilla, R. S. Pilla, 2001 .

A Nonparametric Statistical Approach to Clustering via Mode Identification

Surajit Ray, Jia Li, Bruce G. Lindsay, 2007, J. Mach. Learn. Res..

Application of Maximum Likelihood Methods to Population Genetic Data for the Estimation of Individual Fertilities

Kathryn Roeder, Bruce G. Lindsay, K. Roeder, 1989 .

Exponential family mixture models (with least-squares estimators)

B. Lindsay, 1986 .

Uniqueness of estimation and identifiability in mixture models

K. Roeder, B. Lindsay, 1993 .

Moment-based oscillation properties of mixture models

Kathryn Roeder, Bruce G. Lindsay, K. Roeder, 1997 .

Gene capture prediction and overlap estimation in EST sequencing from one or multiple libraries

Ji-Ping Z. Wang, Bruce G. Lindsay, Liying Cui, 2005, BMC Bioinformatics.

Improving mixture tree construction using better EM algorithms

Shu-Chuan (Grace) Chen, Bruce Lindsay, B. Lindsay, 2014, Comput. Stat. Data Anal..

EST clustering error evaluation and correction

Ji-Ping Z. Wang, Bruce G. Lindsay, James Leebens-Mack, 2004, Bioinform..

Building mixture trees from binary sequence data

Bruce G. Lindsay, B. Lindsay, Shu-Chuan Chen, 2006 .

Robust Estimation and Tests based on Quadratic Inference Function

B. Lindsay, 2001 .

On mixtures of hazards: Nonparametric maximum likelihood in certain competing risk failure models

B. Lindsay, J. Lynch, Hsiu-Li Hsi, 1992 .

Discussion: Semiparametric mixture models

B. Lindsay, 1991 .

Errors in Inspection: Integer Parameter Maximum Likelihood in a Finite Population

Bruce G. Lindsay, B. Lindsay, 1985 .

TO DETECT DIFFERENCES OF BYCATCH RATES IN SURFACE AND SUBSURFACE DRIFfNETS IN NORTH PACIFIC SQUID DRIFfNET FISHERIES

B. Lindsay, J., 2010 .

Modal simulation and visualization in finite mixture models

B. Lindsay, Daeyoung Kim, 2011 .

Comparing Wald and Likelihood Regions Applied to Locally Identifiable Mixture Models

Bruce G. Lindsay, Daeyoung Kim, B. Lindsay, 2011 .

A Simple and Accurate Method for Approximate Conditional Inference Applied to Exponential Family Models

B. Lindsay, R. Waterman, 1996 .

Inference Functions and Quadratic Score Tests

Bruce G. Lindsay, Annie Qu, B. Lindsay, 2003 .

Projections on cones, chi-bar squared distributions, and Weyl's formula

Bruce G. Lindsay, Yong Lin, B. Lindsay, 1997 .

Aitken-based acceleration methods for assessing convergence of multilayer neural networks

Sagar V. Kamarthi, Bruce G. Lindsay, Ramani S. Pilla, 2001, IEEE Trans. Neural Networks.

Estimating the number of classes in multiple populations: A geometric analysis

B. Lindsay, C. Mao, 2004 .

Quadratic distances on probabilities: A unified foundation

Marianthi Markatou, Surajit Ray, Bruce G. Lindsay, 2008, 0804.0991.

Local modal regression

Runze Li, Weixin Yao, Bruce G Lindsay, 2012, Journal of nonparametric statistics.

Moment-Based Approximations of Distributions Using Mixtures: Theory and Applications

Bruce G. Lindsay, Ramani S. Pilla, B. Lindsay, 2000 .

Bayesian Mixture Labeling by Highest Posterior Density

B. Lindsay, W. Yao, 2009 .

Works Title Bayesian Mixture Labeling by Highest Posterior Density Permalink

B. Lindsay, 2008 .

The iteratively reweighted estimating equation in minimum distance problems

Bruce G. Lindsay, Ayanendranath Basu, B. Lindsay, 2004, Comput. Stat. Data Anal..

Efficiency of the Conditional Score in a Mixture Setting

B. G. Lindsay, B. Lindsay, 1983 .

Composite Likelihood Inference in a Discrete Latent Variable Model for Two-Way “Clustering-by-Segmentation” Problems

Francesco Bartolucci, Francesca Chiaromonte, Prabhani Kuruppumullage Don, 2015, 1506.08278.

Improving cross-validated bandwidth selection using subsampling-extrapolation techniques

Qing Wang, Bruce G. Lindsay, B. Lindsay, 2015, Comput. Stat. Data Anal..

Nuisance parameters, mixture models, and the efficiency of partial likelihood estimators

B. G. Lindsay, B. Lindsay, 1980, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

The residual adjustment function and weighted likelihood: a graphical interpretation of robustness of minimum disparity estimators

Bruce G. Lindsay, Chanseok Park, Ayanendranath Basu, 2002 .

Statistical Distances and Their Role in Robustness

Marianthi Markatou, Yang Chen, Bruce G. Lindsay, 2016, 1612.07408.

Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests

Marianthi Markatou, Surajit Ray, Bruce G. Lindsay, 2014, Journal of the American Statistical Association.

Tests and diagnostics for heterogeneity in the species problem

Bruce G. Lindsay, Chang Xuan Mao, B. Lindsay, 2003, Comput. Stat. Data Anal..

Weighted Likelihood Equations with Bootstrap Root Search

Marianthi Markatou, Bruce G. Lindsay, Ayanendranath Basu, 1998 .

Efficiency of projected score methods in rectangular array asymptotics

Bruce G. Lindsay, Richard P. Waterman, B. Lindsay, 2003 .

Model selection in high dimensions: a quadratic‐risk‐based approach

B. Lindsay, Surajit Ray, 2006, math/0611544.

DEGREES OF FREEDOM IN QUADRATIC GOODNESS OF FIT

B. Lindsay, Surajit Ray, M. Markatou, 2022 .

Model diagnostic tests for selecting informative correlation structure in correlated data

B. Lindsay, A. Qu, J. Lee, 2008 .