Download or read book Monotonic Minimax Estimators of a 2x2 Covariance Matrix written by F. Perron and published by . This book was released on 1990 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Minimax Estimators of a Covariance Matrix written by F. Perron and published by . This book was released on 1990 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Another Class of Minimax Estimators of a Variance Covariance Matrix in Multivariate Normal Distribution written by Hisayuki Hara and published by . This book was released on 1998 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Orthogonally Invariant Minimax Estimator of the Covariance Matrix of a Multivariate Normal Population written by STANFORD UNIV CA DEPT OF STATISTICS. and published by . This book was released on 1983 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the problem of estimating the covariance matrix of a multivariate normal population James and Stein obtained a minimax estimator by considering the best invariant estimator with respect to the triangular group. In this paper its authors propose an orthogonally invariant estimator obtained by averaging the minimax estimator with respect to the invariant measure on the orthogonal group. Explicit forms of the proposed estimator are given for dimensions 2 and 3. Risk is evaluated for various population covariance matrices and it shows a substantial improvement over the minimax estimator for a wide range of population covariance matrices. (Author).
Download or read book Minimax Estimation of a Multivariate Normal Mean with Unknown Covariance Matrix written by Leon Jay Gleser and published by . This book was released on 1976 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Let X be a p-variate (p>or=3) vector, normally distributed with unknown mean theta and unknown covariance matrix sigma. Let W:pXp be distributed independently of X, and let W have a Wishart distribution with n degrees of freedom and parameter sigma. It is desired to estimate theta under the quadratic loss (delta-theta)'Q(delta-theta), where Q is a known positive definite matrix. Under the condition that a lower bound for the smallest characteristic root of Q sigma is known, a family of minimax estimators is developed.
Download or read book Admissible Minimax Estimators of the Mean of a Multivariate Normal Distribution for Unknown Covariance Matrix written by Khursheed Alam and published by . This book was released on 1973 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book High Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Download or read book Minimax Estimation of a Normal Mean Vector when the Covariance Matrix is Unknown written by L. J. Gleser and published by . This book was released on 1977 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Gamma minimax Estimators for the Mean of a Multivariate Normal Distribution with Partially Unknown Covariance Matrix written by Lanxiang Chen and published by . This book was released on 1987 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Class of minimax estimators of a normal mean vector for arbitrary quadratic loss and unknown covariance matrix written by James O. Berger and published by . This book was released on 1981 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Download or read book Generalized Least Squares written by Takeaki Kariya and published by John Wiley & Sons. This book was released on 2004-11-19 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalised Least Squares adopts a concise and mathematically rigorous approach. It will provide an up-to-date self-contained introduction to the unified theory of generalized least squares estimations, adopting a concise and mathematically rigorous approach. The book covers in depth the 'lower and upper bounds approach', pioneered by the first author, which is widely regarded as a very powerful and useful tool for generalized least squares estimation, helping the reader develop their understanding of the theory. The book also contains exercises at the end of each chapter and applications to statistics, econometrics, and biometrics, enabling use for self-study or as a course text.
Download or read book NBS Special Publication written by and published by . This book was released on 1970 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Download or read book Convex Optimization written by Stephen P. Boyd and published by Cambridge University Press. This book was released on 2004-03-08 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Download or read book An Author and Permuted Title Index to Selected Statistical Journals written by Brian L. Joiner and published by . This book was released on 1970 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: All articles, notes, queries, corrigenda, and obituaries appearing in the following journals during the indicated years are indexed: Annals of mathematical statistics, 1961-1969; Biometrics, 1965-1969#3; Biometrics, 1951-1969; Journal of the American Statistical Association, 1956-1969; Journal of the Royal Statistical Society, Series B, 1954-1969,#2; South African statistical journal, 1967-1969,#2; Technometrics, 1959-1969.--p.iv.
