Download or read book Nonparametric Inferences on Conditional Quantile Processes written by Chuan Goh and published by . This book was released on 2007 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is concerned with tests of restrictions on the sample path of conditional quantile processes. These tests are tantamount to assessments of lack of fit for models of conditional quantile functions or more generally as tests of how certain covariates affect the distribution of an outcome variable of interest. This paper extends tests of the generalized likelihood ratio (GLR) type as introduced by Fan, Zhang and Zhang (2001) to nonparametric inference problems regarding conditional quantile processes. As such, the tests proposed here present viable alternatives to existing methods based on the Khmaladze (1981, 1988) martingale transformation. The range of inference problems that may be addressed by the methods proposed here is wide, and includes tests of nonparametric nulls against nonparametric alternatives as well as tests of parametric specifications against nonparametric alternatives. In particular, it is shown that a class of GLR statistics based on nonparametric additive quantile regressions have pivotal asymptotic distributions given by the suprema of squares of Bessel processes, as in Hawkins (1987) and Andrews (1993). The tests proposed here are also shown to be asymptotically rate-optimal for nonparametric hypothesis testing according to the formulations of Ingster (1993) and of Spokoiny (1996).
Download or read book Conditional Quantile Processes Based on Series Or Many Regressors written by Alexandre Belloni and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR-series framework, covering many regressors as a special case, for performing inference on the entire conditional quantile function and its linear functionals. In this framework, we approximate the entire conditional quantile function by a linear combination of series terms with quantile-specific coefficients and estimate the function-valued coefficients from the data. We develop large sample theory for the QR-series coefficient process, namely we obtain uniform strong approximations to the QR-series coefficient process by conditionally pivotal and Gaussian processes. Based on these two strong approximations, or couplings, we develop four resampling methods (pivotal, gradient bootstrap, Gaussian, and weighted bootstrap) that can be used for inference on the entire QR-series coefficient function. We apply these results to obtain estimation and inference methods for linear functionals of the conditional quantile function, such as the conditional quantile function itself, its partial derivatives, average partial derivatives, and conditional average partial derivatives. Specifically, we obtain uniform rates of convergence and show how to use the four resampling methods mentioned above for inference on the functionals. All of the above results are for function-valued parameters, holding uniformly in both the quantile index and the covariate value, and covering the pointwise case as a by-product. We demonstrate the practical utility of these results with an empirical example, where we estimate the price elasticity function and test the Slutsky condition of the individual demand for gasoline, as indexed by the individual unobserved propensity for gasoline consumption.
Download or read book Nonparametric Inference Based on Conditional Moment Inequalities written by Donald W. K. Andrews and published by . This book was released on 2013 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is established. The false coverage probabilities and power of the CS's and tests are established for fixed alternatives and some local alternatives. Finite-sample simulation results are given for a nonparametric conditional quantile model with censoring and a nonparametric conditional treatment effect model. The recommended CS/test uses a Cramér-von-Mises-type test statistic and employs a generalized moment selection critical value.
Download or read book Nonparametric Econometric Methods and Application written by Thanasis Stengos and published by MDPI. This book was released on 2019-05-20 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few.
Download or read book Fractional Order Statistic Approximation for Nonparametric Conditional Quantile Inference written by Matt Goldman and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Selected topics on nonparametric conditional quantiles and risk theory written by Yebin Cheng and published by Rozenberg Publishers. This book was released on 2007 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Nonparametric Analysis of Conditional Distributions and Inference for Poisson Point Processes written by Matthew Alan Taddy and published by . This book was released on 2008 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Inference for Extremal Conditional Quantiles written by Daisuke Kurisu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Inference written by Z. Govindarajulu and published by World Scientific Publishing Company Incorporated. This book was released on 2007-01-01 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area. With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.
Download or read book Nonparametric Econometric Methods written by Qi Li and published by Emerald Group Publishing. This book was released on 2009-12-04 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.
Download or read book NONPARAMETRIC INFERENCE FOR EXTREMAL CONDITIONAL QUANTILES written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Quantile Regression written by Roger Koenker and published by CRC Press. This book was released on 2017-10-12 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
Download or read book A Study of Nonparametric Inference Problems Using Monte Carlo Methods written by Hoi-Sheung Ho and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "A Study of Nonparametric Inference Problems Using Monte Carlo Methods" by Hoi-sheung, Ho, 何凱嫦, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of the thesis entitled A STUDY OF NONPARAMETRIC INFERENCE PROBLEMS USING MONTE CARLO METHODS submitted by Ho, Hoi Sheung for the degree of Doctor of Philosophy at The University of Hong Kong in December 2005 This study considered the problem of constructing condence intervals for non- standard interest parameters, such as the population quantile and the density function, based on random samples from univariate data distributions. The pri- mary objective is to generate improved condence intervals with higher coverage accuracy. In all the proposed methods, advanced Edgeworth expansions for ap- propriate distribution functions were established to derive the optimal coverage probabilitiesoftheintervals, andsimulationstudieswereconductedtoinvestigate the small-sample e(R)ects. The interval estimation problem was then extended to a regression setup, and the focus shifted to the more ambitious goal of carrying out nonparametric conditional inference for regression coecients. In the quantile case, rst an advanced bootstrap method which combines the itechniques of smoothing and iteration, was developed and shown to successfully improve the coverage accuracies of the bootstrap percentile and the bootstrap- t intervals for population quantiles. Second three di(R)erent methods of coverage calibration of simple linear interpolated intervals were proposed and shown to yield asymptotically more accurate coverage probabilities. In the density function case, a non-standard iterated bootstrap procedure whichrequiresbothunsmoothedandsmoothedouterbootstrapsamplesforboot- strapping kernel density estimates and relevant biases respectively, was proposed to reduce the coverage error of the bootstrap-t interval considerably. Finally, this study investigated the problem of constructing condence sets for regression coecients, conditional on an observed ancillary statistic, where the unknown error distribution is specied nonparametrically. The conditional asymptoticnormalityoftheregressioncoecientestimatorsunderregularitycon- ditions was established and the approach of plugging in kernel density estimators in conditional condence procedures was justied formally. ii DOI: 10.5353/th_b3577447 Subjects: Nonparametric statistics Monte Carlo method Confidence intervals
Download or read book Nonparametric Estimation of Conditional Quantile Function written by Ashis Kumar Gangopadhyay and published by . This book was released on 1987 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2011-10-09 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
Download or read book New Theory and Methods for High Order Accurate Inference on Quantile Treatment Effects and Conditional Quantiles written by David M. Kaplan and published by . This book was released on 2013 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation concerns methods for inference on quantiles in various models. Methods that are asymptotically justified may still be quite inaccurate in finite samples. To improve the state of the art, I explore different theoretical approaches for achieving higher-order accuracy: fractional order statistic theory based on exact finite-sample distributions in Chapters 1 and 2, and Edgeworth expansions and fixed-smoothing asymptotics in Chapter 3. For each of the different practical methods proposed, I examine accuracy via precise theoretical results as well as simulations. The family of methods using interpolated duals of exact-analytic L-statistics (IDEAL) covers unconditional (one-sample and two-sample treatment/control, Ch. 1) and nonparametric conditional (Ch. 2) models, and it offers improvements over the existing literature in terms of accuracy, robustness, and/or computation time. The Edgeworth-based method improves upon related prior methods and is a good alternative for quantiles too far into the tails for IDEAL to handle.
Download or read book Essays in Honor of Aman Ullah written by R. Carter Hill and published by Emerald Group Publishing. This book was released on 2016-06-29 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 36 of Advances in Econometrics recognizes Aman Ullah's significant contributions in many areas of econometrics and celebrates his long productive career.
