The Robustness Of Model Selection Rules PDF Download
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Author | : Jochen A. Jungeilges |
Publisher | : LIT Verlag Münster |
Total Pages | : 276 |
Release | : 1992 |
Genre | : Business & Economics |
ISBN | : |
Download The Robustness of Model Selection Rules Book in PDF, ePub and Kindle
Author | : Jochen A. Jungeilges |
Publisher | : |
Total Pages | : 88 |
Release | : 1989 |
Genre | : |
ISBN | : |
Download On the Robustness of a Class of Model Selection Rules Book in PDF, ePub and Kindle
Author | : Lars Peter Hansen |
Publisher | : Princeton University Press |
Total Pages | : 453 |
Release | : 2016-06-28 |
Genre | : Business & Economics |
ISBN | : 0691170975 |
Download Robustness Book in PDF, ePub and Kindle
The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.
Author | : Robert L. Launer |
Publisher | : |
Total Pages | : 330 |
Release | : 1979 |
Genre | : Mathematics |
ISBN | : |
Download Robustness in Statistics Book in PDF, ePub and Kindle
An introduction to robust estimation; The robustness of residual displays; Robust smoothing; Robust pitman-like estimators; Robust estimation in the presence of outliers; Study of robustness by simulation: particularly improvement by adjustment and combination; Robust techniques for the user; Application of robust regression to trajectory data reduction; Tests for censoring of extreme values (especially) when population distributions are incompletely defined; Robust estimation for time series autoregressions; Robust techniques in communication; Robustness in the strategy of scientific model building; A density-quantile function perspective on robust.
Author | : Md. Jafar Ahmed Khan |
Publisher | : |
Total Pages | : 155 |
Release | : 2007 |
Genre | : |
ISBN | : 9780494267363 |
Download Robust Linear Model Selection for High-dimensional Datasets Book in PDF, ePub and Kindle
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.
Author | : Eric Neumayer |
Publisher | : Cambridge University Press |
Total Pages | : 269 |
Release | : 2017-08-17 |
Genre | : Business & Economics |
ISBN | : 1108415393 |
Download Robustness Tests for Quantitative Research Book in PDF, ePub and Kindle
This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.
Author | : Walter Daelemans |
Publisher | : Springer Science & Business Media |
Total Pages | : 714 |
Release | : 2008-09-04 |
Genre | : Computers |
ISBN | : 354087478X |
Download Machine Learning and Knowledge Discovery in Databases Book in PDF, ePub and Kindle
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.
Author | : Robert L. Launer |
Publisher | : Academic Press |
Total Pages | : 313 |
Release | : 2014-05-12 |
Genre | : Mathematics |
ISBN | : 1483263363 |
Download Robustness in Statistics Book in PDF, ePub and Kindle
Robustness in Statistics contains the proceedings of a Workshop on Robustness in Statistics held on April 11-12, 1978, at the Army Research Office in Research Triangle Park, North Carolina. The papers review the state of the art in statistical robustness and cover topics ranging from robust estimation to the robustness of residual displays and robust smoothing. The application of robust regression to trajectory data reduction is also discussed. Comprised of 14 chapters, this book begins with an introduction to robust estimation, paying particular attention to iteration schemes and error structure of estimators. Sensitivity and influence curves as well as their connection with jackknife estimates are described. The reader is then introduced to a simple analog of trimmed means that can be used for studying residuals from a robust point-of-view; a class of robust estimators (called P-estimators) based on the location and scale-invariant Pitman estimators of location; and robust estimation in the presence of outliers. Subsequent chapters deal with robust regression and its use to reduce trajectory data; tests for censoring of extreme values, especially when population distributions are incompletely defined; and robust estimation for time series autoregressions. This monograph should be of interest to mathematicians and statisticians.
Author | : Le Chang |
Publisher | : |
Total Pages | : 0 |
Release | : 2017 |
Genre | : |
ISBN | : |
Download Essays on Robust Model Selection and Model Averaging for Linear Models Book in PDF, ePub and Kindle
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.
Author | : Léna Soler |
Publisher | : Springer Science & Business Media |
Total Pages | : 377 |
Release | : 2012-03-23 |
Genre | : Science |
ISBN | : 9400727585 |
Download Characterizing the Robustness of Science Book in PDF, ePub and Kindle
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.