A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

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Gaussian Processes for Machine Learning

This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to be drawn robustly. The properties of the QMLE and the information matrix are exploited to yield several useful tests for model misspecification.

https://ecsocman.hse.ru/data/774/042/1218/white_maximum_likekihood_estimation.pdf

Maximum likelihood estimation of misspecified models

The Cox regression model for censored survival data specifies that covariates have a proportional effect on the hazard function of the life-time distribution of an individual. In this paper we discuss how this model can be extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process. This permits a statistical regression analysis of the intensity of a recurrent event allowing for complicated censoring patterns and time dependent covariates. Furthermore, this formulation gives rise to proofs with very simple structure using martingale techniques for the asymptotic properties of the estimators from such a model. Finally an example of a statistical analysis is included.

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Cox's Regression Model for Counting Processes: A Large Sample Study

Asymptotic distribution theory is the primary method used to examine the properties of econometric estimators and tests We present conditions for obtaining cosistency and asymptotic normality of a very general class of estimators (extremum estimators) Consistent asymptotic variance estimators are given to enable approximation of the asymptotic distribution Asymptotic efficiency is another desirable property then considered Throughout the chapter, the general results are also specialized to common econometric estimators (eg MLE and GMM), and in specific examples we work through the conditions for the various results in detail The results are also extended to two-step estimators (with finite-dimensional parameter estimation in the first step), estimators derived from nonsmooth objective functions, and semiparametric two-step estimators (with nonparametric estimation of an infinite-dimensional parameter in the first step) Finally, the trinity of test statistics is considered within the quite general setting of GMM estimation, and numerous examples are given

Chapter 36 Large sample estimation and hypothesis testing

We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.

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A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models

THE ELEMENTS OF INTEGRATION. Measurable Functions. Measures. The Integral. Integrable Functions. The Lebesgue Spaces L p . Modes of Convergence. Decomposition of Measures. Generation of Measures. Product Measures. THE ELEMENTS OF LEBESGUE MEASURE. Volumes of Cells and Intervals. The Outer Measure. Measurable Sets. Examples of Measurable Sets. Approximation of Measurable Sets. Additivity and Nonadditivity. Nonmeasurable and Non--Borel Sets. References. Index.

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The elements of integration and Lebesgue measure

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Bartle

Papers

The elements of integration and Lebesgue measure

The Elements Of Integration