Results 281 to 290 of about 23,103 (324)

Detecting Model Misspecification in Bayesian Piecewise Growth Models

Structural Equation Modeling: A Multidisciplinary Journal, 2022
Bayesian estimation has become increasingly more popular with piecewise growth models because it can aid in accurately modeling nonlinear change over time.
S. Depaoli, Fan Jia, Ihnwhi Heo
semanticscholar   +1 more source

Reducing Model Misspecification and Bias in the Estimation of Interactions

Political Analysis, 2021
Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments.
M. Blackwell, Michael Olson
semanticscholar   +1 more source

The Effects of Misspecification of a Regression Model

Biometrical Journal, 1987
AbstractThe consequences of the misspecification of a regression model are considered. For small effects of covariates a proportional consistency theorem is derived. The consistent estimation of the covariance matrix of the estimates is discussed.
Nagelkerke, N. J. D., Hart, A. A. M., Oosting, J.   +2 more
openaire   +1 more source

The Misspecification of Arma Models

Statistica Neerlandica, 1989
The object of this paper is to assess the effects of fitting a model of the wrong order to a time series which is generated by an autoregressive moving–average process. The method is to examine the spectral density functions which are indicated by the probability limits of the least–squares estimators of the misspecified models.
openaire   +2 more sources

Simulation-based Bayesian inference under model misspecification

arXiv.org
Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulations is relatively straightforward.
Ryan P. Kelly, David J. Warne, David Frazier, D. Nott, Michael U. Gutmann, Christopher C. Drovandi   +5 more
semanticscholar   +1 more source

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation

arXiv.org
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation program (simulation-based inference; SBI).
M. Schmitt, Paul-Christian Burkner, Ullrich Kothe, Stefan T. Radev   +3 more
semanticscholar   +1 more source

Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors

Scandinavian Actuarial Journal, 2020
In the context of predicting future claims, a fully Bayesian analysis – one that specifies a statistical model, prior distribution, and updates using Bayes's formula – is often viewed as the gold-standard, while Bühlmann's credibility estimator serves as
Liang Hong, Ryan Martin
semanticscholar   +1 more source

Position Estimation under Model Misspecification

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017
When time-based radio range measurements between network nodes are perturbed by a line-of-sight blocking obstacle, position estimation accuracy degrades significantly. The perturbation is caused by multipath propagation or excess delays since waves travel at slower speed while piercing the obstacles.
Mendrzik, Rico, Bauch, Gerhard
openaire   +1 more source

Model misspecification in Data Envelopment Analysis

Annals of Operations Research, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Testing for Misspecification in Models of Spatial Flows [PDF]

Environment and Planning A: Economy and Space, 1987
Methods of assessing model fit for models of spatial flows frequently do not take account of spatial structure. A nonparametric test, based on the signs of the residuals from a fit, is presented for detecting patterns in the residuals. It can be thought of as a general test of misspecification that can allow for spatial structure effects.
openaire   +1 more source