Applying Robust Methods to Operational Risk Modeling
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Truncation and Censoring
Truncation and Censoring Laura Magazzini [email protected] Laura Magazzini (@univr.it) Truncation and Censoring 1 / 40 Truncation and censoring Truncation and censoring Truncation: sample data are drawn from a subset of a larger population of interest . Characteristic of the distribution from which the sample data are drawn . Example: studies of income based on incomes above or below the poverty line (of limited usefulness for inference about the whole population) Censoring: values of the dependent variable in a certain range are all transformed to (or reported at) a single value . Defect in the sample data . Example: in studies of income, people below the poverty line are reported at the poverty line Truncation and censoring introduce similar distortion into conventional statistical results Laura Magazzini (@univr.it) Truncation and Censoring 2 / 40 Truncation and censoring Truncation Truncation Aim: infer the caracteristics of a full population from a sample drawn from a restricted population . Example: characteristics of people with income above $100,000 Let Y be a continous random variable with pdf f (y). The conditional distribution of y given y > a (a a constant) is: f (y) f (yjy > a) = Pr(y > a) In case of y normally distributed: 1 φ x−µ f (yjy > a) = σ σ 1 − Φ(α) a−µ where α = σ Laura Magazzini (@univr.it) Truncation and Censoring 3 / 40 Truncation and censoring Truncation Moments of truncated distributions E(Y jy < a) < E(Y ) E(Y jy > a) > E(Y ) V (Y jtrunc:) < V (Y ) Laura Magazzini (@univr.it) Truncation and Censoring -
Estimation of Panel Data Regression Models with Two-Sided Censoring Or Truncation
Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation Sule Alan, Bo E. Honoré, Luojia Hu, and Søren Leth–Petersen Federal Reserve Bank of Chicago Reserve Federal WP 2011-08 Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation Sule Alany Bo E. Honoréz Luojia Hu x Søren Leth–Petersen { November 14, 2011 Abstract This paper constructs estimators for panel data regression models with individual speci…c heterogeneity and two–sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re–censored or re–truncated residuals. While these moment conditions do not identify the parameter of interest, they can be used to motivate objective functions that do. We apply one of the estimators to study the e¤ect of a Danish tax reform on household portfolio choice. The idea behind the estimators can also be used in a cross sectional setting. Key Words: Panel Data, Censored Regression, Truncated Regression. JEL Code: C20, C23, C24. This research was supported by NSF Grant No. SES-0417895 to Princeton University, the Gregory C. Chow Econometric Research Program at Princeton University, and the Danish National Research Foundation, through CAM at the University of Copenhagen (Honoré) and the Danish Social Science Research Council (Leth–Petersen). We thank Christian Scheuer and numerous seminar participants for helpful comments. The opinions expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System. yFaculty of Economics, University of Cambridge, Sidgwick Avenue, Cambridge, UK, CB3 9DD. -
Outlier Impact and Accommodation on Power Hongjing Liao Beijing Foreign Studies University, [email protected]
Journal of Modern Applied Statistical Methods Volume 16 | Issue 1 Article 15 5-1-2017 Outlier Impact and Accommodation on Power Hongjing Liao Beijing Foreign Studies University, [email protected] Yanju Li Western Carolina University, Cullowhee, NC, [email protected] Gordon P. Brooks Ohio University, [email protected] Follow this and additional works at: http://digitalcommons.wayne.edu/jmasm Part of the Applied Statistics Commons, Social and Behavioral Sciences Commons, and the Statistical Theory Commons Recommended Citation Liao, H., Yanju, Li., & Brooks, G. P. (2017). Outlier impact and accommodation on power. Journal of Modern Applied Statistical Methods, 16(1), 261-278. doi: 10.22237/jmasm/1493597640 This Regular Article is brought to you for free and open access by the Open Access Journals at DigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an authorized editor of DigitalCommons@WayneState. Outlier Impact and Accommodation on Power Cover Page Footnote This research was supported by School of English for Specific urP poses, Beijing Foreign Studies University, grant ZJ1513 This regular article is available in Journal of Modern Applied Statistical Methods: http://digitalcommons.wayne.edu/jmasm/vol16/ iss1/15 Journal of Modern Applied Statistical Methods Copyright © 2017 JMASM, Inc. May 2017, Vol. 16, No. 1, 261-278. ISSN 1538 − 9472 doi: 10.22237/jmasm/1493597640 Outlier Impact and Accommodation on Power Hongjing Liao Yanju Li Gordon P. Brooks Beijing Foreign Studies University Western Carolina University Ohio University Beijing, China Cullowhee, NC Athens, OH The outliers’ influence on power rates in ANOVA and Welch tests at various conditions was examined and compared with the effectiveness of nonparametric methods and Winsorizing in minimizing the impact of outliers. -
Best-Practice Recommendations for Defining, Identifying, and Handling
Article Organizational Research Methods 16(2) 270-301 ª The Author(s) 2013 Best-Practice Reprints and permission: sagepub.com/journalsPermissions.nav Recommendations for DOI: 10.1177/1094428112470848 orm.sagepub.com Defining, Identifying, and Handling Outliers Herman Aguinis1, Ryan K. Gottfredson1, and Harry Joo1 Abstract The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier defi- nitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, inter- esting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommenda- tions can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research. -
Regularization of Case-Specific Parameters for Robustness And
Regularization of Case-Specific Parameters for Robustness and Efficiency Yoonkyung Lee, Steven N. MacEachern, and Yoonsuh Jung The Ohio State University, USA Summary. Regularization methods allow one to handle a variety of inferential problems where there are more covariates than cases. This allows one to consider a potentially enormous number of covari- ates for a problem. We exploit the power of these techniques, supersaturating models by augmenting the “natural” covariates in the problem with an additional indicator for each case in the data set. We attach a penalty term for these case-specific indicators which is designed to produce a desired effect. For regression methods with squared error loss, an ℓ1 penalty produces a regression which is robust to outliers and high leverage cases; for quantile regression methods, an ℓ2 penalty decreases the variance of the fit enough to overcome an increase in bias. The paradigm thus allows us to robustify procedures which lack robustness and to increase the efficiency of procedures which are robust. We provide a general framework for the inclusion of case-specific parameters in regularization problems, describing the impact on the effective loss for a variety of regression and classification problems. We outline a computational strategy by which existing software can be modified to solve the augmented regularization problem, providing conditions under which such modification will converge to the opti- mum solution. We illustrate the benefits of including case-specific parameters in the context of mean regression and quantile regression through analysis of NHANES and linguistic data sets. Keywords: Case indicator; Large margin classifier; LASSO; Leverage point; Outlier; Penal- ized method; Quantile regression 1. -
Censored Data and Truncated Distributions William Greene
19 Censored Data and Truncated Distributions William Greene Abstract We detail the basic theory for regression models in which dependent variables are censored or underlying distributions are truncated. The model is extended to models for counts, sample selection models, and hazard models for duration data. Entry-level theory is presented for the practitioner. We then describe a few of the recent, frontier developments in theory and practice. 19.1 Introduction 695 19.2 Truncation 697 19.3 Censored data and the censored regression model 701 19.3.1 Estimation and inference 704 19.3.2 Specification analysis 705 19.3.3 Heteroskedasticity 706 19.3.4 Unobserved heterogeneity 707 19.3.5 Distribution 707 19.3.6 Other models with censoring 708 19.4 Incidental truncation and sample selection 712 19.5 Panel data 715 19.5.1 Estimating fixed effects models 716 19.5.2 Estimating random effects models 719 19.5.3 An application of fixed and random effects estimators 719 19.5.4 Sample selection models for panel data 721 19.6 Recent developments 724 19.7 Summary and conclusions 728 19.1 Introduction The analysis of censoring and truncation arises not from a free-standing body of theory and economic/econometric modeling, but from a subsidiary set of results that treat a practical problem of how data are gathered and analyzed. Thus, we have chosen the title ‘‘Censored Data and Truncated Distributions’’ for this chapter, rather than the more often used rubric ‘‘Limited Dependent Variables’’ 695 696 Censored Data and Truncated Distributions (see, e.g., Maddala, 1983) specifically to underscore the relationship between the results and this practical issue. -
Statistical Inference with Paired Observations and Independent Observations in Two Samples
Statistical inference with paired observations and independent observations in two samples Benjamin Fletcher Derrick A thesis submitted in partial fulfilment of the requirements of the University of the West of England, Bristol, for the degree of Doctor of Philosophy Applied Statistics Group, Engineering Design and Mathematics, Faculty of Environment and Technology, University of the West of England, Bristol February, 2020 Abstract A frequently asked question in quantitative research is how to compare two samples that include some combination of paired observations and unpaired observations. This scenario is referred to as ‘partially overlapping samples’. Most frequently the desired comparison is that of central location. Depend- ing on the context, the research question could be a comparison of means, distributions, proportions or variances. Approaches that discard either the paired observations or the independent observations are customary. Existing approaches evoke much criticism. Approaches that make use of all of the available data are becoming more prominent. Traditional and modern ap- proaches for the analyses for each of these research questions are reviewed. Novel solutions for each of the research questions are developed and explored using simulation. Results show that proposed tests which report a direct measurable difference between two groups provide the best solutions. These solutions advance traditional methods in this area that have remained largely unchanged for over 80 years. An R package is detailed to assist users to per- form these new tests in the presence of partially overlapping samples. Acknowledgments I pay tribute to my colleagues in the Mathematics Cluster and the Applied Statistics Group at the University of the West of England, Bristol. -
Influential Observations and Inference in Accounting Research
Influential Observations and Inference in Accounting Research Andrew J. Leone University Miami [email protected] Miguel Minutti-Meza University of Miami [email protected] Charles Wasley University of Rochester [email protected] October 2013 (First Draft: April 2012) Abstract The existence of potentially influential or outlier observations is ubiquitous in empirical accounting research. The purpose of this paper is to summarize the various methods used by accounting researchers to identify and control for influential observations; assess the effectiveness of such methods and of alternative methods from the statistics literature; and, provide guidance for future studies so they may systematically identify and mitigate the impact of influential observations. A survey of articles published in accounting journals shows considerable variation in the way researchers deal with influential observations prior to estimating a regression model. We demonstrate that the common approach of winsorizing each variable has only a modest impact on parameter estimates compared to “doing nothing”. We also demonstrate that truncation tends to bias coefficients toward zero. More generally, we show that both winsorizing and truncating do little to accommodate potential bias caused by unusual and infrequent events (i.e., data points that are not random errors). Alternatively, use of robust regression significantly reduces such bias and is preferable to winsorization or truncation when regression is used for hypothesis testing. In addition, robust regression methods are available in commonly-used statistical packages and they do not entail the ad hoc choice of winsorization or truncation rules, thus providing a convenient way to control for influential observations and enhance inter-study comparability. -
Speaking Stata: Trimming to Taste
The Stata Journal Editors H. Joseph Newton Nicholas J. Cox Department of Statistics Department of Geography Texas A&M University Durham University College Station, Texas Durham, UK [email protected] [email protected] Associate Editors Christopher F. Baum, Boston College Frauke Kreuter, Univ. of Maryland–College Park Nathaniel Beck, New York University Peter A. Lachenbruch, Oregon State University Rino Bellocco, Karolinska Institutet, Sweden, and Jens Lauritsen, Odense University Hospital University of Milano-Bicocca, Italy Stanley Lemeshow, Ohio State University Maarten L. Buis, WZB, Germany J. Scott Long, Indiana University A. Colin Cameron, University of California–Davis Roger Newson, Imperial College, London Mario A. Cleves, University of Arkansas for Austin Nichols, Urban Institute, Washington DC Medical Sciences Marcello Pagano, Harvard School of Public Health William D. Dupont , Vanderbilt University Sophia Rabe-Hesketh, Univ. of California–Berkeley Philip Ender , University of California–Los Angeles J. Patrick Royston, MRC Clinical Trials Unit, David Epstein, Columbia University London Allan Gregory, Queen’s University Philip Ryan, University of Adelaide James Hardin, University of South Carolina Mark E. Schaffer, Heriot-Watt Univ., Edinburgh Ben Jann, University of Bern, Switzerland Jeroen Weesie, Utrecht University Stephen Jenkins, London School of Economics and Ian White, MRC Biostatistics Unit, Cambridge Political Science Nicholas J. G. Winter, University of Virginia Ulrich Kohler , University of Potsdam, Germany Jeffrey Wooldridge, Michigan State University Stata Press Editorial Manager Stata Press Copy Editors Lisa Gilmore David Culwell and Deirdre Skaggs The Stata Journal publishes reviewed papers together with shorter notes or comments, regular columns, book reviews, and other material of interest to Stata users. -
ELKI: a Large Open-Source Library for Data Analysis
ELKI: A large open-source library for data analysis ELKI Release 0.7.5 “Heidelberg” https://elki-project.github.io/ Erich Schubert Arthur Zimek Technische Universität Dortmund University of Southern Denmark 1 Introduction This paper documents the release of the ELKI data mining framework, version 0.7.5. ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI oers data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions of additional methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms. We will rst outline the motivation for this release, the plans for the future, and then give a brief overview over the new functionality in this version. We also include an appendix presenting an overview on the overall implemented functionality. 2 ELKI 0.7.5: “Heidelberg” The majority of the work in this release was done at Heidelberg University, both by students (that for example contributed many unit tests as part of Java programming practicals) and by Dr. Erich Schubert. The last ocial release 0.7.1 is now three years old. Some changes in Java have broken this old release, and it does not run on Java 11 anymore (trivial xes are long available, but there has not been a released version) — three years where we have continuted to add functionality, improve APIs, x bugs, ...yet surprisingly many users still use the old version rather than building the latest development version from git (albeit conveniently available at Github, https://github.com/elki-project/elki). -
Optimally Combining Censored and Uncensored Datasets
OPTIMALLY COMBINING CENSORED AND UNCENSORED DATASETS PAUL J. DEVEREUX AND GAUTAM TRIPATHI Abstract. Economists and other social scientists often encounter data generating mechanisms (dgm's) that produce censored or truncated observations. These dgm's induce a probability distribution on the realized observations that di®ers from the underlying distribution for which inference is to be made. If this dichotomy between the target and realized populations is not taken into account, statistical inference can be severely biased. In this paper, we show how to do e±cient semiparametric inference in moment condition models by supplementing the incomplete observations with some additional data that is not subject to censoring or truncation. These additional observations, or refreshment samples as they are sometimes called, can often be obtained by creatively matching existing datasets. To illustrate our results in an empirical setting, we show how to estimate the e®ect of changes in com- pulsory schooling laws on age at ¯rst marriage, a variable that is censored for younger individuals. We also demonstrate how refreshment samples for this application can be created by matching cohort information across census datasets. 1. Introduction In applied research, economists often face situations in which they have access to two datasets that they can use but one set of data su®ers from censoring or truncation. In some cases, especially if the censored sample is larger, researchers use it and attempt to deal with the problem of partial observation in some manner1. In other cases, economists simply use the clean sample, i.e., the dataset not subject to censoring or truncation, and ignore the censored one so as to avoid biases. -
Limited Dependent Variables—Truncation, Censoring, and Sample Selection
19 LIMITED DEPENDENT VARIABLes—TRUNCATION, CENSORING, AND SAMP§LE SELECTION 19.1 INTRODUCTION This chapter is concerned with truncation and censoring. As we saw in Section 18.4.6, these features complicate the analysis of data that might otherwise be amenable to conventional estimation methods such as regression. Truncation effects arise when one attempts to make inferences about a larger population from a sample that is drawn from a distinct subpopulation. For example, studies of income based on incomes above or below some poverty line may be of limited usefulness for inference about the whole population. Truncation is essentially a characteristic of the distribution from which the sample data are drawn. Censoring is a more common feature of recent studies. To continue the example, suppose that instead of being unobserved, all incomes below the poverty line are reported as if they were at the poverty line. The censoring of a range of values of the variable of interest introduces a distortion into conventional statistical results that is similar to that of truncation. Unlike truncation, however, censoring is a feature of the sample data. Presumably, if the data were not censored, they would be a representative sample from the population of interest. We will also examine a form of truncation called the sample selection problem. Although most empirical work in this area involves censoring rather than truncation, we will study the simpler model of truncation first. It provides most of the theoretical tools we need to analyze models of censoring and sample selection. The discussion will examine the general characteristics of truncation, censoring, and sample selection, and then, in each case, develop a major area of application of the principles.