Results 1 to 10 of about 29,799 (296)

A new robust and most powerful test in the presence of local misspecification

Communications in Statistics - Theory and Methods, 2017
Gabriel V. Montes Rojas, Anil K. Bera, Walter Sosa-Escudero   +2 more
exaly  

Misspecification Analysis

1990
openaire   +1 more source

Modeling Misspecification as a Parameter in Bayesian Structural Equation Models

Educational and Psychological Measurement
James Ohisei Uanhoro
exaly  

Diagnostics of Model Misspecification

2006
openaire   +1 more source

Minimizing sensitivity to model misspecification [PDF]

Quantitative Economics, 2022
We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments.
Weidner, Martin, Bonhomme, Stéphane
core   +10 more sources

Decision-Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets [PDF]

Entropy
Distributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical distribution or a model. To further guard against finite, noisy data, model-
Charita Dellaporta, Patrick O’Hara, Theodoros Damoulas   +2 more
doaj   +2 more sources

Identifying the sources of model misspecification [PDF]

Journal of Monetary Economics, 2020
The first and third authors acknowledge financial support from the National Science Foundation through grants 102159 and 1022125, respectively.
Barbara Rossi, Chun-Hung Kuo, Atsushi Inoue   +2 more
exaly   +7 more sources

Surveillance of Pharmaceutical Risk‐Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data [PDF]

Learning Health Systems
Introduction Active post‐marketing surveillance of prescribing behavior of high‐risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real‐world data are underdeveloped.
Harris Butler, John D. Rice, Nichole E. Carlson, Elaine H. Morrato   +3 more
doaj   +2 more sources

Generalized Information Matrix Tests for Detecting Model Misspecification

Econometrics, 2016
Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies.
Richard Golden, Halbert White, Steven S Henley   +2 more
exaly   +3 more sources

Assessing the impact of variance heterogeneity and misspecification in mixed-effects location-scale models [PDF]

BMC Medical Research Methodology
Purpose Linear Mixed Model (LMM) is a common statistical approach to model the relation between exposure and outcome while capturing individual variability through random effects.
Vincent Jeanselme, Marco Palma, Jessica Barrett   +2 more
doaj   +2 more sources