Tag: Nonparametric

Frontiers in Major League Baseball Nonparametric Analysis of Performance Using Data Envelopment Analysis


Free Download John Ruggiero, "Frontiers in Major League Baseball: Nonparametric Analysis of Performance Using Data Envelopment Analysis"
English | 2010 | pages: 156 | ISBN: 1441908307, 1461427347 | PDF | 0,9 mb
This book focuses on the application of Data Envelopment Analysis (DEA) to Major League Baseball (MLB). DEA is a nonparametric linear programming model that is used across academic disciplines. In sports economics, authors have applied the technique primarily to assess team and/or managerial efficiency. The basis for performance analysis is economic production theory, where it is assumed that baseball can be viewed as a production process whereby inputs (player quality measures) are transformed into outputs (wins, attendance). The primary advantage that DEA has over more traditional regression based approaches is the ability to handle multiple inputs and multiple outputs. Further, the approach is nonparametric and hence, does not require a priori specification of the production function. The book develops the theory of DEA in the context of a production environment. A focal point is the assessment of technical and cost efficiency of MLB teams. It is shown that previous frontier applications that measure efficiency provide biased results given that the outcome of a game is zero-sum. If a team loses a game due to inefficiency, another team wins a lost game. A corrected frontier is presented to overcome this problem. Free agent salary arbitration is analyzed using a dual DEA model. Each free agent’s contract zone is identified. The upper and lower bounds, representing the player’s and team’s perspective of value, respectively, are estimated. Player performance is estimated using a modified DEA model to rank order players based on multiple attributes. This model will be used to evaluate current Hall of Fame players. We provide arguments for other players who are deserving of membership. We also use our measure of performance and evaluate age-performance profilers for many ball players. Regression analysis is used to identify the age of peak performance. The method is used to evaluate some of the all-time greats. We also use the method to analyze admitted and implicated steroid users. The results clearly show that performance was enhanced. This book will provide appropriate theoretical models with methodological considerations and interesting empirical analyses and is intended to serve academics and practitioners interested in applying DEA to baseball as well as other sports or production processes.

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Flexible Nonparametric Curve Estimation


Free Download Flexible Nonparametric Curve Estimation
English | 2024 | ISBN: 3031665007 | 312 Pages | PDF EPUB (True) | 51 MB
Through a series of carefully crafted chapters, the theoretical foundations of flexible nonparametric estimators are examined, complemented by comprehensive numerical studies. From theorem elucidation to practical applications, the text provides a deep dive into the intricacies of nonparametric curve estimation.

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Nonparametric Analysis of Univariate Heavy‐Tailed Data Research and Practice


Free Download Nonparametric Analysis of Univariate Heavy‐Tailed Data: Research and Practice by Natalia Markovich
English | PDF | 2007 | 328 Pages | ISBN : 0470510870 | 32.1 MB
Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer’s condition, possible non-existence of some moments, and sparse observations in the tail of the distribution.

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Principles of Nonparametric Learning


Free Download Principles of Nonparametric Learning by László Györfi
English | PDF (True) | 2002 | 344 Pages | ISBN : 3211836888 | 24 MB
The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions.

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