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The present monograph is a synthesis of what has been contributed during the last decade to the analysis of market demand in large econ­ omies where consumers may have non-convex preference relations. Al­ though research in this field has not yet come to an end there exists a variety of interesting results, established in different frameworks by means of different conceptual and formal tools. a It is my aim to give comprehensive treatment of the existing lit­ erature including my own contributions. In working out differences and interrelations of the various ap­ proaches I adopted and modified several of the original results. My desire to present the problem and the methods by which it has been treated in such a way, that also non-specialists can follow, con­ flicted sometimes with the inevitable complexity of tools to be used. Therefore, I decided to give enough room to the introductory and prepa­ ratory part of this work. This part consists of the introduction and of the first four chapters. The main part of the present analysis consists of chapters 5 to 7.

Quantitative methods (economics) --- Demand (Economic theory) --- Smoothing (Statistics) --- Mathematical models --- Business & Economics --- Economic Theory --- Demand (Economic theory) - Mathematical models

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Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is given to the implementation of presented methods in Matlab. All created programs are included in a special toolbox which is an integral part of the book. This toolbox contains many Matlab scripts useful for kernel smoothing of density, cumulative distribution function, regression function, hazard funct

Smoothing (Statistics) --- Kernel functions. --- Functions, Kernel --- Functions of complex variables --- Geometric function theory --- Curve fitting --- Graduation (Statistics) --- Roundoff errors --- Statistics

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A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regressionSmoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis.Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include:* Comprehensive coverage of smoothing and regression with software hints and applications from a wide variety of disciplines* A unified, easy-to-follow format* Contributions from more than 25 leading researchers from around the world* More than 150 illustrations also covering new graphical techniques important for exploratory data analysis and visualization of high-dimensional problems* Extensive end-of-chapter referencesFor professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.

Mathematical statistics --- 519.234 --- Smoothing (Statistics) --- Nonparametric statistics --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Curve fitting --- Graduation (Statistics) --- Roundoff errors --- Statistics --- Non-parametric methods --- Nonparametric statistics. --- Regression analysis. --- Smoothing (Statistics). --- 519.234 Non-parametric methods

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Smoothing (Statistics) --- Estimation theory --- Analysis of variance --- 519.2 --- Probability. Mathematical statistics --- 519.2 Probability. Mathematical statistics --- Curve fitting --- Graduation (Statistics) --- Roundoff errors --- Statistics --- Estimating techniques --- Least squares --- Mathematical statistics --- Stochastic processes --- ANOVA (Analysis of variance) --- Variance analysis --- Experimental design

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Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence.

Analysis of variance. --- Smoothing (Statistics). --- Spline theory. --- Analysis of variance --- Spline theory --- Smoothing (Statistics) --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Spline functions --- ANOVA (Analysis of variance) --- Variance analysis --- Statistics. --- Statistical Theory and Methods. --- Curve fitting --- Graduation (Statistics) --- Roundoff errors --- Statistics --- Approximation theory --- Interpolation --- Mathematical statistics --- Experimental design --- Mathematical statistics. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics