Nonparametric Estimation Under Shape Constraints
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| Author | : Piet Groeneboom |
| Publisher | : Cambridge University Press |
| Total Pages | : 429 |
| Release | : 2014-12-11 |
| Genre | : Mathematics |
| ISBN | : 1316194124 |
This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Among the topics treated are current status and interval censoring models, competing risk models, and deconvolution. Methods of order restricted inference are used in computing maximum likelihood estimators and developing distribution theory for inverse problems of this type. The authors have been active in developing these tools and present the state of the art and the open problems in the field. The earlier chapters provide an introduction to the subject, while the later chapters are written with graduate students and researchers in mathematical statistics in mind. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.
| Author | : Sam Efromovich |
| Publisher | : CRC Press |
| Total Pages | : 867 |
| Release | : 2018-03-12 |
| Genre | : Mathematics |
| ISBN | : 135167983X |
This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.
| Author | : David Ruppert |
| Publisher | : Cambridge University Press |
| Total Pages | : 410 |
| Release | : 2003-07-14 |
| Genre | : Mathematics |
| ISBN | : 9780521785167 |
Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The 2003 book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.
| Author | : Abhishek Bhattacharya |
| Publisher | : Cambridge University Press |
| Total Pages | : 252 |
| Release | : 2012-04-05 |
| Genre | : Mathematics |
| ISBN | : 1107019583 |
Ideal for statisticians, this book will also interest probabilists, mathematicians, computer scientists, and morphometricians with mathematical training. It presents a systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important applications in medical diagnostics, image analysis and machine vision.
| Author | : Daniel J. Henderson |
| Publisher | : Cambridge University Press |
| Total Pages | : 381 |
| Release | : 2015-01-19 |
| Genre | : Business & Economics |
| ISBN | : 110701025X |
The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.
| Author | : Thijs ten Raa |
| Publisher | : Springer Nature |
| Total Pages | : 766 |
| Release | : 2019-12-13 |
| Genre | : Business & Economics |
| ISBN | : 3030237273 |
This Handbook takes an econometric approach to the foundations of economic performance analysis. The focus is on the measurement of efficiency, productivity, growth and performance. These concepts are commonly measured residually and difficult to quantify in practice. In real-life applications, efficiency and productivity estimates are often quite sensitive to the models used in the performance assessment and the methodological approaches adopted by the analysis. The Palgrave Handbook of Performance Analysis discusses the two basic techniques of performance measurement – deterministic benchmarking and stochastic benchmarking – in detail, and addresses the statistical techniques that connect them. All chapters include applications and explore topics ranging from the output/input ratio to productivity indexes and national statistics.
| Author | : |
| Publisher | : |
| Total Pages | : 388 |
| Release | : 1972 |
| Genre | : |
| ISBN | : |
;Contents: Isotonic regression; Estimation under order restrictions; Testing the equality of ordered means--likelihood ratio tests in the normal case; Testing the equality of ordered means--extensions and generalizations; Estimation of distributions; Isotonic tests for goodness of fit; Conditional expectation given a sigma-lattice.
| Author | : International Statistical Institute |
| Publisher | : |
| Total Pages | : 620 |
| Release | : 2003 |
| Genre | : Statistics |
| ISBN | : |
V. 1-5, v. 7-10 include "Bulletin bibliographique."
| Author | : |
| Publisher | : |
| Total Pages | : 888 |
| Release | : 2008 |
| Genre | : Mathematics |
| ISBN | : |
| Author | : Jaromír Antoch |
| Publisher | : Springer |
| Total Pages | : 207 |
| Release | : 2018-05-04 |
| Genre | : Mathematics |
| ISBN | : 9783319846170 |
This volume collects authoritative contributions on analytical methods and mathematical statistics. The methods presented include resampling techniques; the minimization of divergence; estimation theory and regression, eventually under shape or other constraints or long memory; and iterative approximations when the optimal solution is difficult to achieve. It also investigates probability distributions with respect to their stability, heavy-tailness, Fisher information and other aspects, both asymptotically and non-asymptotically. The book not only presents the latest mathematical and statistical methods and their extensions, but also offers solutions to real-world problems including option pricing. The selected, peer-reviewed contributions were originally presented at the workshop on Analytical Methods in Statistics, AMISTAT 2015, held in Prague, Czech Republic, November 10-13, 2015.
