A global sensitivity test for evaluating statistical hypotheses with nonidentifiable models

Biometrics

Volumn 66, Issue 2, 2010, Pages 558-566

  Todem, D ^a   Fine, J ^b   Peng, L ^c  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^c EMORY UNIVERSITY   (United States)

Author keywords

Data missing not at random; Functional estimators; Infimum statistic; Model misspecification; Nonparametric bootstrap; Profile estimators; Sensitivity analysis; Uniform convergence

Indexed keywords

STATISTICS;

DATA MISSING; DATA MISSING NOT AT RANDOM; FUNCTIONAL ESTIMATOR; INFIMUM STATISTIC; MISSING NOT AT RANDOM; MODEL MISSPECIFICATION; NON-PARAMETRIC BOOTSTRAPS; PROFILE ESTIMATOR; SENSITIVITY PARAMETERS; UNIFORM CONVERGENCE;

SENSITIVITY ANALYSIS;

BOOTSTRAPPING; COVARIANCE ANALYSIS; HYPOTHESIS TESTING; META-ANALYSIS; SENSITIVITY ANALYSIS; THEORETICAL STUDY;

HUMAN; LONGITUDINAL STUDY; PROCEDURES; PSYCHIATRY; PSYCHOLOGICAL MODEL; PUBLISHING; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

HUMANS; LONGITUDINAL STUDIES; METHODS; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; PSYCHIATRY; PUBLICATION BIAS; SENSITIVITY AND SPECIFICITY;

EID: 77952981982     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01290.x     Document Type: Article

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