Results 11 to 20 of about 16,381 (262)

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

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

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

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

Modeling Model Misspecification in Structural Equation Models

Stats, 2023
Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent.
Alexander Robitzsch, Robitzsch Alexander   +1 more
exaly   +3 more sources

Convex Models, MLS and Misspecification

Annals of Statistics, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
exaly   +3 more sources

In models we trust: preregistration, large samples, and replication may not suffice

Frontiers in Psychology, 2023
Despite discussions about the replicability of findings in psychological research, two issues have been largely ignored: selection mechanisms and model assumptions.
Martin Spiess, Pascal Jordan
doaj   +1 more source

Effect of Probability Distribution of the Response Variable in Optimal Experimental Design with Applications in Medicine

Mathematics, 2021
In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s ...
Sergio Pozuelo-Campos, Víctor Casero-Alonso, Mariano Amo-Salas   +2 more
doaj   +1 more source

Dynamic Concern for Misspecification [PDF]

Proceedings of the 24th ACM Conference on Economics and Computation, 2023
I consider an agent who posits a set of probabilistic models for the payoff‐relevant outcomes. The agent has a prior over this set but fears the actual model is omitted and hedges against this possibility. The concern for misspecification is endogenous: If a model explains the previous observations well, the concern attenuates.
openaire   +1 more source