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Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing Paid Search Advertising Partial Covariance
2016/1/20
In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically,we employ the partial covariances be...
A $k$-modal probability distribution over the domain $\{1,...,n\}$ is one whose histogram has at most $k$ "peaks" and "valleys." Such distributions are natural generalizations of monotone ($k=0$) and ...
A nonparametric empirical Bayes framework for large-scale multiple testing
Dirichlet process marginal likelihood mixture model
2011/7/6
We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-n...
Adjusting for selection bias in testing multiple families of hypotheses
false discovery rate family-wise error rate hierarchical testing
2011/7/6
In many large multiple testing problems the hypotheses are divided into families. Given the data, families with evidence for true discoveries are selected, and hypotheses within them are tested.
Modern Sequential Analysis and its Applications to Computerized Adaptive Testing
sequential analysis computerized adaptive testing mastery testing
2011/7/5
After a brief review of recent advances in sequential analysis involving sequential generalized likelihood ratio tests, we discuss their use in psychometric testing and extend the asymptotic optimalit...
Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/21
Rejoinder to "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Limiting Laws of Coherence of Random Matrices with Applications to Testing Covariance Structure and Construction of Compressed Sensing Matrices
Chen-Stein method coherence compressed sensing matrix covariance struc-ture law of large numbers limiting distribution maxima moderate deviations mutual incoherence property random matrix sample correlation matrix
2011/3/21
Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimat...
Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Statistical Genetic Studies
2011/3/21
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Statistical Genetic Studies
2011/3/21
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Limiting Laws of Coherence of Random Matrices with Applications to Testing Covariance Structure and Construction of Compressed Sensing Matrices
Chen-Stein method coherence compressed sensing matrix covariance struc-ture law of large numbers limiting distribution maxima moderate deviations mutual incoherence property random matrix sample correlation matrix
2011/3/23
Testing covariance structure is of significant interest in many areas of statistical analysis and construction of compressed sensing matrices is an important problem in signal processing. Motivated b...