Evaluating Statistical Hypotheses Using Weakly‐Identifiable Estimating Functions

DOI10.1111/j.1467-9469.2012.00811.xzbMath1328.62260OpenAlexW1499117579WikidataQ42161187 ScholiaQ42161187MaRDI QIDQ4923053

Guanqun Cao, Jason P. Fine, David Todem, Lijian Yang

Publication date: 5 June 2013

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3685206

zbMATH Keywords

global sensitivity analysisestimating equationsmodel misspecificationmissing not at randompseudolikelihoodinfimum and supremum statistics

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Nonparametric estimation (62G05) Nonparametric statistical resampling methods (62G09)

Cites Work

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• On asymptotically optimal tests under loss of identifiability in semiparametric models
• Pseudo maximum likelihood estimation: Theory and applications
• Testing the order of a model using locally conic parametrization: Population mixtures and stationary ARMA processes
• Missing at random, likelihood ignorability and model completeness.
• Generalized score test of homogeneity for mixed effects models
• Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of Censoring
• A Global Sensitivity Test for Evaluating Statistical Hypotheses with Nonidentifiable Models
• Likelihood ratio tests in curved exponential families with nuisance parameters present only under the alternative
• Hypothesis Testing When a Nuisance Parameter is Present Only Under the Alternative
• Analysis of Longitudinal Data with Non-Ignorable Non-Monotone Missing Values
• Marginal models for the analysis of longitudinal measurements with nonignorable non-monotone missing data
• A resampling method based on pivotal estimating functions
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis
• Maximum Likelihood Estimation of Misspecified Models