scholarly journals Robust Estimation with Discrete Explanatory Variables

Compstat ◽  
2002 ◽  
pp. 509-514 ◽  
Author(s):  
Pavel Čížek
Keyword(s):  
Robust Estimation ◽  
Explanatory Variables

scholarly journals Penalized multiply robust estimation in high-order autoregressive processes with missing explanatory variables

2021 ◽  
pp. 104867
Author(s):  
Wei Xiong ◽  
Dehui Wang ◽  
Dianliang Deng ◽  
Xinyang Wang ◽  
Wanying Zhang
Keyword(s):  
Robust Estimation ◽  
High Order ◽  
Autoregressive Processes ◽  
Explanatory Variables

scholarly journals Parameter Choice, Stability and Validity for Robust Cluster Weighted Modeling

Stats ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 602-615
Author(s):  
Andrea Cappozzo ◽  
Luis Angel García García Escudero ◽  
Francesca Greselin ◽  
Agustín Mayo-Iscar
Keyword(s):  
Statistical Inference ◽  
Robust Estimation ◽  
Final Solution ◽  
Parameter Choice ◽  
Subjective Judgment ◽  
Explanatory Variables ◽  
Weighted Model ◽  
Cluster A ◽  
Parameter Values ◽  
Robust Cluster

Statistical inference based on the cluster weighted model often requires some subjective judgment from the modeler. Many features influence the final solution, such as the number of mixture components, the shape of the clusters in the explanatory variables, and the degree of heteroscedasticity of the errors around the regression lines. Moreover, to deal with outliers and contamination that may appear in the data, hyper-parameter values ensuring robust estimation are also needed. In principle, this freedom gives rise to a variety of “legitimate” solutions, each derived by a specific set of choices and their implications in modeling. Here we introduce a method for identifying a “set of good models” to cluster a dataset, considering the whole panorama of choices. In this way, we enable the practitioner, or the scientist who needs to cluster the data, to make an educated choice. They will be able to identify the most appropriate solutions for the purposes of their own analysis, in light of their stability and validity.

scholarly journals Parametric Versus Semi and Nonparametric Regression Models

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Hamdy F. F. Mahmoud
Keyword(s):  
Nonparametric Regression ◽  
Robust Estimation ◽  
Regression Models ◽  
Linear Models ◽  
Kernel Smoothing ◽  
Estimation Method ◽  
Parametric Models ◽  
Estimation Methods ◽  
Explanatory Variables ◽  
Local Linear

There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about the form of the relationship between the response variable and explanatory variables, and the random error distribution that is assumed. Researchers need to be familiar with each modeling approach requirements. In this paper, differences between these models, common estimation methods, robust estimation, and applications are introduced. For parametric models, there are many known methods of estimation, such as least squares and maximum likelihood methods which are extensively studied but they require strong assumptions. On the other hand, nonparametric regression models are free of assumptions regarding the form of the response-explanatory variables relationships but estimation methods, such as kernel and spline smoothing are computationally expensive and smoothing parameters need to be obtained. For kernel smoothing there two common estimators: local constant and local linear smoothing methods. In terms of bias, especially at the boundaries of the data range, local linear is better than local constant estimator.  Robust estimation methods for linear models are well studied, however the robust estimation methods in nonparametric regression methods are limited. A robust estimation method for the semiparametric and nonparametric regression models is introduced.

Language in mental retardation: Individual and syndromic differences, and neurogenetic variation 1Based on a keynote presentation at the Third European Conference on Psychological Theory and Research in Mental Retardation, Geneva, September 1st, 2000.

2001 ◽  
Vol 60 (3) ◽  
pp. 161-178 ◽  
Author(s):  
Jean A. Rondal
Keyword(s):  
Mental Retardation ◽  
Language Development ◽  
Oral Language ◽  
Psychological Theory ◽  
Genetic Syndromes ◽  
Explanatory Variables ◽  
Keynote Presentation ◽  
Genetic Origins ◽  
The Sixties ◽  
The Individual

Predominantly non-etiological conceptions have dominated the field of mental retardation (MR) since the discovery of the genetic etiology of Down syndrome (DS) in the sixties. However, contemporary approaches are becoming more etiologically oriented. Important differences across MR syndromes of genetic origin are being documented, particularly in the cognition and language domains, differences not explicable in terms of psychometric level, motivation, or other dimensions. This paper highlights the major difficulties observed in the oral language development of individuals with genetic syndromes of mental retardation. The extent of inter- and within-syndrome variability are evaluated. Possible brain underpinnings of the behavioural differences are envisaged. Cases of atypically favourable language development in MR individuals are also summarized and explanatory variables discussed. It is suggested that differences in brain architectures, originating in neurological development and having genetic origins, may largely explain the syndromic as well as the individual within-syndrome variability documented. Lastly, the major implications of the above points for current debates about modularity and developmental connectionism are spelt out.

A Strategy for Analysing Multiple Risk Factors with Application to Cervical Pain Syndrome

1989 ◽  
Vol 28 (01) ◽  
pp. 14-19 ◽  
Author(s):  
J. F. Dartigues ◽  
Ph. Peytour ◽  
E. Puymirat ◽  
P. Henry ◽  
M. Gagnon ◽  
...  
Keyword(s):  
Risk Factors ◽  
Low Power ◽  
Head Trauma ◽  
Pain Syndrome ◽  
Psychological Status ◽  
Confounding Factors ◽  
Cervical Pain ◽  
Extreme Form ◽  
Explanatory Variables ◽  
Multiple Risk Factors

Abstract:When studying the possible effects of several factors in a given disease, two major problems arise: (1) confounding, and (2) multiplicity of tests. Frequently, in order to cope with the problem of confounding factors, models with multiple explanatory variables are used. However, the correlation structure of the variables may be such that the corresponding tests have low power: in its extreme form this situation is coined by the term “multicollinearity”. As the problem of multiplicity is still relevant in these models, the interpretation of results is, in most cases, very hazardous. We propose a strategy - based on a tree structure of the variables - which provides a guide to the interpretation and controls the risk of erroneously rejecting null hypotheses. The strategy was applied to a study of cervical pain syndrome involving 990 subjects and 17 variables. Age, sex, head trauma, posture at work and psychological status were all found to be important risk factors.

Robust Estimation of Integrated Hydraulics and Parameters in Water Distribution Systems

2001 ◽  
Author(s):  
Mietek A. Brdys ◽  
Kazimierz Duzinkiewicz ◽  
Michal Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Robust Estimation ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems
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