The Robustness Of Model Selection Rules
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The Robustness Of Model Selection Rules
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Author : Jochen A. Jungeilges
language : en
Publisher: LIT Verlag Münster
Release Date : 1992
The Robustness Of Model Selection Rules written by Jochen A. Jungeilges and has been published by LIT Verlag Münster this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Business & Economics categories.
On The Robustness Of A Class Of Model Selection Rules
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Author : Jochen A. Jungeilges
language : en
Publisher:
Release Date : 1989
On The Robustness Of A Class Of Model Selection Rules written by Jochen A. Jungeilges and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with categories.
Robust Linear Model Selection For High Dimensional Datasets
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Author : Md. Jafar Ahmed Khan
language : en
Publisher:
Release Date : 2007
Robust Linear Model Selection For High Dimensional Datasets written by Md. Jafar Ahmed Khan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
We consider two different strategies for model selection: (a) one-step model building and (b) two-step model building. For one-step model building, we robustify the step-bystep algorithms forward selection (FS) and stepwise (SW), with robust partial F-tests as stopping rules.
Essays On Robust Model Selection And Model Averaging For Linear Models
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Author : Le Chang
language : en
Publisher:
Release Date : 2017
Essays On Robust Model Selection And Model Averaging For Linear Models written by Le Chang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Model selection is central to all applied statistical work. Selecting the variables for use in a regression model is one important example of model selection. This thesis is a collection of essays on robust model selection procedures and model averaging for linear regression models. In the first essay, we propose robust Akaike information criteria (AIC) for MM-estimation and an adjusted robust scale based AIC for M and MM-estimation. Our proposed model selection criteria can maintain their robust properties in the presence of a high proportion of outliers and the outliers in the covariates. We compare our proposed criteria with other robust model selection criteria discussed in previous literature. Our simulation studies demonstrate a significant outperformance of robust AIC based on MM-estimation in the presence of outliers in the covariates. The real data example also shows a better performance of robust AIC based on MM-estimation. The second essay focuses on robust versions of the "Least Absolute Shrinkage and Selection Operator" (lasso). The adaptive lasso is a method for performing simultaneous parameter estimation and variable selection. The adaptive weights used in its penalty term mean that the adaptive lasso achieves the oracle property. In this essay, we propose an extension of the adaptive lasso named the Tukey-lasso. By using Tukey's biweight criterion, instead of squared loss, the Tukey-lasso is resistant to outliers in both the response and covariates. Importantly, we demonstrate that the Tukey-lasso also enjoys the oracle property. A fast accelerated proximal gradient (APG) algorithm is proposed and implemented for computing the Tukey-lasso. Our extensive simulations show that the Tukey-lasso, implemented with the APG algorithm, achieves very reliable results, including for high-dimensional data where p>n. In the presence of outliers, the Tukey-lasso is shown to offer substantial improvements in performance compared to the adaptive lasso and other robust implementations of the lasso. Real data examples further demonstrate the utility of the Tukey-lasso. In many statistical analyses, a single model is used for statistical inference, ignoring the process that leads to the model being selected. To account for this model uncertainty, many model averaging procedures have been proposed. In the last essay, we propose an extension of a bootstrap model averaging approach, called bootstrap lasso averaging (BLA). BLA utilizes the lasso for model selection. This is in contrast to other forms of bootstrap model averaging that use AIC or Bayesian information criteria (BIC). The use of the lasso improves the computation speed and allows BLA to be applied even when the number of variables p is larger than the sample size n. Extensive simulations confirm that BLA has outstanding finite sample performance, in terms of both variable and prediction accuracies, compared with traditional model selection and model averaging methods. Several real data examples further demonstrate an improved out-of-sample predictive performance of BLA.
On Robust Model Selection Within The Cox Model
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Author : Tadeusz Bednarski
language : en
Publisher:
Release Date : 2007
On Robust Model Selection Within The Cox Model written by Tadeusz Bednarski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Model selection methods have shown to be useful in the process of econometric modelling. The paper studies robust Akaike-Schwarz type information criteria of model choice within the Cox model. The criteria are based on a smooth modification of the partial likelihood function. Apart from asymptotic results, a Monte Carlo study is presented, which shows the finite sample behaviour of the procedure under discrepancies from the Cox model. Analysis of a real unemployment data case is also included.
Machine Learning And Knowledge Discovery In Databases
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Author : Walter Daelemans
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-04
Machine Learning And Knowledge Discovery In Databases written by Walter Daelemans and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-04 with Computers categories.
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Robust Model Selection In Dynamic Models With An Application To Comparing Predictive Accuracy
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Author : Nicholas M. Kiefer
language : en
Publisher:
Release Date : 2006
Robust Model Selection In Dynamic Models With An Application To Comparing Predictive Accuracy written by Nicholas M. Kiefer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
A model selection procedure based on a general criterion function, with an example of the Kullback-Leibler Information Criterion (KLIC) using quasi-likelihood functions, is considered for dynamic non-nested models. We propose a robust test which generalizes Lien and Vuong's (1987) test with a Heteroscadasticity/Autocorrelation (HAC) variance estimator. We use the fixed-b asymptotics developed in Kiefer and Vogelsang (2005) to improve the asymptotic approximation to the sampling distribution of the test statistic. The fixed-b approach is compared with a bootstrap method and the standard normal approximation in Monte Carlo simulations. The fixed-b asymptotics and the bootstrap method are found to be markedly superior to the standard normal approximation. An empirical application for foreign exchange rate forecasting models is presented.
Robustness Tests For Quantitative Research
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Author : Eric Neumayer
language : en
Publisher: Cambridge University Press
Release Date : 2017-08-17
Robustness Tests For Quantitative Research written by Eric Neumayer and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-17 with Business & Economics categories.
This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.
Robustness In Econometrics
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Author : Vladik Kreinovich
language : en
Publisher: Springer
Release Date : 2017-02-11
Robustness In Econometrics written by Vladik Kreinovich and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-11 with Technology & Engineering categories.
This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.
Characterizing The Robustness Of Science
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Author : Léna Soler
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-23
Characterizing The Robustness Of Science written by Léna Soler and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-23 with Science categories.
Mature sciences have been long been characterized in terms of the “successfulness”, “reliability” or “trustworthiness” of their theoretical, experimental or technical accomplishments. Today many philosophers of science talk of “robustness”, often without specifying in a precise way the meaning of this term. This lack of clarity is the cause of frequent misunderstandings, since all these notions, and that of robustness in particular, are connected to fundamental issues, which concern nothing less than the very nature of science and its specificity with respect to other human practices, the nature of rationality and of scientific progress; and science’s claim to be a truth-conducive activity. This book offers for the first time a comprehensive analysis of the problem of robustness, and in general, that of the reliability of science, based on several detailed case studies and on philosophical essays inspired by the so-called practical turn in philosophy of science.