Local Regression and Likelihood. Authors: Loader, Clive. Statistics and Computing.
Local Regression and Likelihood. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software. Show all. Table of contents (13 chapters). The Origins of Local Regression. Local Regression Methods. The examples in this book use locfit in S (or S-Plus), which will be of use to many readers given the widespread availability of S within the statistics community. This book, and the associated software, have grown out of the author’s work in the eld of local regression over the past several years.
Local Regression and Likelihood book. Local Regression and Likelihood (Statistics and Computing). 0387987754 (ISBN13: 9780387987750).
Local likelihood was introduced by Tibshirani and Hastie as a method of smoothing by local polynomials in non-Gaussian regression models
Local likelihood was introduced by Tibshirani and Hastie as a method of smoothing by local polynomials in non-Gaussian regression models. In this paper an extension of these methods to density estimation is discussed, and comparison with other methods of density estimation presented. The local likelihood method has particularly strong advantages over kernel methods when estimating tails of densities and in multivariate settings. Suppose constraints are incorporated in a simple manner. Asymptotic properties of the estimate are discussed.
This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT. Springer-Verlag, New York, 1999. I have UF Library copy. Nonparametric Estimation of Probability Densities and Regression Curves. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1989. Essential Wavelets for Statistical Applications and Data Analysis. Birkh¨ auser, Boston, 1997. Multivariate Density Estimation : Theory, Practice, and Visualization. Wiley, New York, 1992. Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC Press, 1986.
Likelihood (and by extension log-likelihood) is one of the most important concepts in statistics. Its used for everything. For your first point, likelihood is not the same as the value of the parameter. Likelihood is the likelihood of the entire model given a set of parameter estimates. It's calculated by taking a set of parameter estimates, calculating the probability density for each one, and then multiplying the probability densities for all the observations together (this follows from probability theory in that P(A and B) P(A)P(B) if A and B are independent). Includes bibliographical references and index. Published 1999 by Springer in New York. Regression analysis, Estimation theory.
Clive Loader, J Chambers, W Eddy. Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation.