scholarly journals Generalized Information Matrix Tests for Detecting Model Misspecification

Econometrics ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 46 ◽  
Author(s):  
Richard Golden ◽  
Steven Henley ◽  
Halbert White ◽  
T. Kashner
Keyword(s):  
Model Misspecification ◽  
Information Matrix ◽  
Generalized Information

scholarly journals Generalized information matrix tests for copulas

2019 ◽  
Vol 38 (9) ◽  
pp. 1024-1054 ◽  
Author(s):  
Artem Prokhorov ◽  
Ulf Schepsmeier ◽  
Yajing Zhu
Keyword(s):  
Information Matrix ◽  
Generalized Information

A generalized information matrix fusion based heterogeneous track-to-track fusion algorithm

2011 ◽  
Author(s):  
Xin Tian ◽  
Yaakov Bar-Shalom ◽  
Ting Yuan ◽  
Erik Blasch ◽  
Khanh Pham ◽  
...  
Keyword(s):  
Information Matrix ◽  
Fusion Algorithm ◽  
Track Fusion ◽  
Generalized Information ◽  
Matrix Fusion

scholarly journals On Lagrange Multiplier Tests in Multidimensional Item Response Theory: Information Matrices and Model Misspecification

2017 ◽  
Vol 78 (4) ◽  
pp. 653-678 ◽  
Author(s):  
Carl F. Falk ◽  
Scott Monroe
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Lagrange Multiplier ◽  
Model Misspecification ◽  
Information Matrix ◽  
Model Specification ◽  
Response Theory ◽  
Monte Carlo Simulation Study ◽  
Lm Tests ◽  
Lm Test

Lagrange multiplier (LM) or score tests have seen renewed interest for the purpose of diagnosing misspecification in item response theory (IRT) models. LM tests can also be used to test whether parameters differ from a fixed value. We argue that the utility of LM tests depends on both the method used to compute the test and the degree of misspecification in the initially fitted model. We demonstrate both of these points in the context of a multidimensional IRT framework. Through an extensive Monte Carlo simulation study, we examine the performance of LM tests under varying degrees of model misspecification, model size, and different information matrix approximations. A generalized LM test designed specifically for use under misspecification, which has apparently not been previously studied in an IRT framework, performed the best in our simulations. Finally, we reemphasize caution in using LM tests for model specification searches.

An Information Matrix Test for the Collapsing of Categories Under the Partial Credit Model

2018 ◽  
Vol 43 (6) ◽  
pp. 721-750
Author(s):  
Daphna Harel ◽  
Russell J. Steele
Keyword(s):  
Goodness Of Fit ◽  
Model Misspecification ◽  
Information Matrix ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Goodness Of Fit Test ◽  
Data Set ◽  
Data Reduction Technique ◽  
And Performance ◽  
Information Matrix Test

Collapsing categories is a commonly used data reduction technique; however, to date there do not exist principled methods to determine whether collapsing categories is appropriate in practice. With ordinal responses under the partial credit model, when collapsing categories, the true model for the collapsed data is no longer a partial credit model, and therefore refitting a partial credit model may result in model misspecification. This article details the implementation and performance of an information matrix test (IMT) to assess the implications of collapsing categories for a given data set under the partial credit model and compares its performance to the application of a nominal response model (NRM) and the S − X2 goodness-of-fit statistic. The IMT and NRM-based test are able to correctly determine the true number of categories for an item, given reasonable power through this goodness-of-fit test. We conclude by applying the test to a well-studied data set from the literature.

scholarly journals How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It

2015 ◽  
Vol 23 (2) ◽  
pp. 159-179 ◽  
Author(s):  
Gary King ◽  
Margaret E. Roberts
Keyword(s):  
Model Misspecification ◽  
Information Matrix ◽  
Theory And Practice ◽  
Standard Errors ◽  
Test Statistic ◽  
Methodological Problems ◽  
Variance Estimates ◽  
Information Matrix Test ◽  
Published Research ◽  
Robust Standard Errors

“Robust standard errors” are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, with or without robust standard errors, will still bias estimators of all but a few quantities of interest. The resulting cavernous gap between theory and practice suggests that considerable gains in applied statistics may be possible. We seek to help researchers realize these gains via a more productive way to understand and use robust standard errors; a new general and easier-to-use “generalized information matrix test” statistic that can formally assess misspecification (based on differences between robust and classical variance estimates); and practical illustrations via simulations and real examples from published research. How robust standard errors are used needs to change, but instead of jettisoning this popular tool we show how to use it to provide effective clues about model misspecification, likely biases, and a guide to considerably more reliable, and defensible, inferences. Accompanying this article is software that implements the methods we describe.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity

2018 ◽  
pp. 150-155
Author(s):  
Muklas Rivai
Keyword(s):  
Electrical Conductivity ◽  
Optimal Design ◽  
Linear Models ◽  
Silicon Oxide ◽  
Information Matrix ◽  
Relevant Information ◽  
Non Linear

Optimal design is a design which required in determining the points of variable factors that would be attempted to optimize the relevant information so that fulfilled the desired criteria. The optimal fulfillment criteria based on the information matrix of the selected model.

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