Results 221 to 230 of about 15,773 (265)

Regression Diagnostic under Model Misspecification

Journal of Applied Statistics, 2007
We propose two novel diagnostic measures for the detection of influential observations for regression parameters in linear regression. Traditional diagnostic statistics focus on the effect of deletion of data points either on parameter estimates, or on predicted values.
Li-Chu, Chien, Tsung-Shan, Tsou
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Model Misspecification and Multipoint Linkage Analysis

Human Heredity, 1992
Pairwise linkage analysis is robust to genetic model misspecification provided dominance is correctly specified, the primary effect being inflation of the recombination fraction. By contrast, we show that multipoint analysis under misspecified models is not robust when a putative disease locus is placed between close flanking markers, with potentially ...
N, Risch, L, Giuffra
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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.
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Misspecification in event studies

Journal of Corporate Finance, 2017
Abstract We examine the statistical error and efficiency associated with two commonly used event-study techniques when applied to samples of various sizes. Previous research has established that the frequently used Patell (1976) test is not well specified when the event itself creates additional return variance.
Joseph M. Marks, Jim Musumeci
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Bootstrapping fMRI Data: Dealing with Misspecification

Neuroinformatics, 2015
The validity of inference based on the General Linear Model (GLM) for the analysis of functional magnetic resonance imaging (fMRI) time series has recently been questioned. Bootstrap procedures that partially avoid modeling assumptions may offer a welcome solution.
Sanne P, Roels, Beatrijs, Moerkerke, Tom, Loeys   +2 more
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Diagnostics of model misspecification

1996
In section 1.2, we denoted the true regression relationship between Y and x by µ(x), and we noted that µ(x) is generally unknown, although in practice it is approximated by a parametric function f (x, θ). Because we use this f to make some statistical inferences, however, we must be able to determine whether or not our choice of f is accurate.
Sylvie Huet, Annie Bouvier, Marie-Anne Gruet, Emmanuel Jolivet   +3 more
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VAR forecasting under misspecification

Journal of Econometrics, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Dynamic Concern for Misspecification

We 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. We show that different
Lanzani, Giacomo, Skrzypacz, Andrzej, Sugaya, Takuo, Ramos, João, Bardhi, Arjada, Board, Simon, Madsen, Erik   +6 more
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Misspecification Tests in Econometrics

1989
Misspecification tests play an important role in detecting unreliable and inadequate economic models. This book brings together many results from the growing literature in econometrics on misspecification testing. It provides theoretical analyses and convenient methods for application.
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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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