A composite spatial predictor via local criteria under a misspecified model

2017 ◽  
Vol 32 (2) ◽  
pp. 341-355 ◽  
Author(s):  
Chun-Shu Chen ◽  
Chao-Sheng Chen
Keyword(s):  
Misspecified Model

3. Multilevel Covariance Structure Analysis by Fitting Multiple Single-Level Models

2007 ◽  
Vol 37 (1) ◽  
pp. 53-82 ◽  
Author(s):  
Ke-Hai Yuan ◽  
Peter M. Bentler
Keyword(s):  
Structure Analysis ◽  
Model Misspecification ◽  
Covariance Structure ◽  
Covariance Structure Analysis ◽  
Test Statistics ◽  
Single Level ◽  
Misspecified Model ◽  
Simultaneous Procedure ◽  
Error Formulas ◽  
Covariance Structure Models

Data in social and behavioral sciences are often hierarchically organized. Multilevel statistical procedures have been developed to analyze such data while taking into account the dependence of observations. When simultaneously evaluating models at all levels, a significant statistic provides no information on the level at which the model is misspecified. Model misspecification can exist at one or several levels simultaneously. When one level is misspecified, the other levels may be affected even when they are correctly specified. Motivated by these observations, we propose to separate a multilevel covariance structure into multiple single-level covariance structure models and to fit these single-level models as in conventional covariance structure analysis. A procedure for segregating the multilevel model into single-level models is developed. Five test statistics for evaluating a model at each level are provided. Standard error formulas for the separate estimators are also provided, and their efficiency is compared to simultaneous estimators. Empirical and Monte Carlo results demonstrate the advantages of the segregated procedure over the simultaneous procedure. Computer programs that will allow the developed procedure to be used in practice are also presented.

A Misspecified Model

1987 ◽  
Vol 3 (2) ◽  
pp. 306-306 ◽  
Author(s):  
Halbert White
Keyword(s):  
Misspecified Model

scholarly journals WHAT HAPPENS WHEN DEMAND IS ESTIMATED WITH A MISSPECIFIED MODEL?*

2008 ◽  
Vol 56 (4) ◽  
pp. 809-839 ◽  
Author(s):  
DONGLING HUANG ◽  
CHRISTIAN ROJAS ◽  
FRANK BASS
Keyword(s):  
Misspecified Model

scholarly journals Gradient-Aware Model-Based Policy Search

2020 ◽  
Vol 34 (04) ◽  
pp. 3801-3808
Author(s):  
Pierluca D'Oro ◽  
Alberto Maria Metelli ◽  
Andrea Tirinzoni ◽  
Matteo Papini ◽  
Marcello Restelli
Keyword(s):  
Transition Model ◽  
Learning Approaches ◽  
Policy Improvement ◽  
Policy Search ◽  
Model Based ◽  
Misspecified Model ◽  
Policy Gradient ◽  
Search Approach ◽  
Available Information ◽  
Novel Model

Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.

scholarly journals EDITOR'S INTRODUCTION

2002 ◽  
Vol 6 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Mark Salmon
Keyword(s):  
Transfer Function ◽  
Loss Function ◽  
Decision Rules ◽  
Common View ◽  
Nominal Model ◽  
Economic Decisions ◽  
Misspecified Model ◽  
The Face ◽  
Simple Bounds ◽  
Input Uncertainties

The papers collected in this issue are united in a common view that it is rational to recognize that we have a poor perception of the constraints we face when making economic decisions and hence we employ decision rules that are robust. Robustness can be interpreted in different ways but generally it implies that our decision rules should not depend critically on an exact description of these constraints but they should perform well over a prespecified range of potential variations in the assumed economic environment. So, we are interested in deriving optimal and hence rational decisions where our utility or loss function incorporates the need for robustness in the face of a misspecified model. This misspecification can involve placing simple bounds on deviations from the parameters we assume for a nominal model, or misspecified dynamics, neglected nonlinearities, time variation, or quite general arbitrary misspecification in the transfer function between the input uncertainties and the output variables in which we are ultimately interested.

A Misspecified Model

1988 ◽  
Vol 4 (2) ◽  
pp. 361-363
Author(s):  
Juan J. Dolado
Keyword(s):  
Misspecified Model

Model Misspecification and Spatial Structure in Spatial-Interaction Models

1983 ◽  
Vol 15 (3) ◽  
pp. 319-327 ◽  
Author(s):  
M Baxter
Keyword(s):  
Spatial Structure ◽  
Spatial Interaction ◽  
Model Misspecification ◽  
Parameter Estimates ◽  
Interaction Models ◽  
Misspecified Models ◽  
Spatial Interaction Models ◽  
Misspecified Model ◽  
Special Cases ◽  
Explicit Expressions

Concern has been expressed about the effects of spatial structure on parameter estimates from spatial-interaction models. The problem is essentially one of model misspecification. With a correctly specified model assumed, in which destination attraction depends on whether it is near to an origin or not, the consequences of using a misspecified model are examined. Explicit expressions for bias in the parameter estimates are derived; these are complex, but depend on terms that can be clearly interpreted in terms of aspects of spatial structure, such as scale, compactness, shape, remoteness of destinations, etc. Some simple special cases show how, with misspecified models, estimates from different systems will almost certainly differ. Extensions of the analysis and problems of estimation and interpretation are discussed.

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