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"Is this sample unusual?"
40 Puzzles and Problems in Probability and Mathematical Statistics
A arte da ciência dos dados em R
A arte da ciência dos dados em RModelos, significado e práticas modernas numa estrutura organizadaNo interior, encontrará:- Uma visita completa aos fluxos de trabalho modernos de ciência de dados usando tidyverse, tidymodels e R Markdown- Exemplos práticos e projectos que utilizam dados do mundo real- Discussão clara de modelação estatística, interpretabilidade e considerações éticas- Orientação sobre como pensar como um cientista de dados, e não apenas agir como umThe Art of Data Science in R não ensina apenas a escrever código - ensina-o a raciocinar com dados.
A arte e a ciência da análise de dados
The Art and Science of Data Analysis fornece um guia completo sobre os princípios e técnicas essenciais da análise de dados. Abrange tópicos fundamentais, como a garantia da qualidade dos dados, a realização de análises exploratórias de dados, a compreensão dos fundamentos estatísticos e a aplicação de métodos analíticos avançados, como a aprendizagem automática, o agrupamento, a classificação e a previsão de séries cronológicas. O livro dá ênfase às aplicações práticas e aos exercícios práticos, realçando a importância das considerações éticas e da aprendizagem contínua. O objetivo é dotar os leitores dos conhecimentos e competências necessários para uma tomada de decisões eficaz baseada em dados e para a inovação em vários domínios.
A Basic Course in Probability Theory
This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. In this second edition, the text has been reorganized for didactic purposes, new exercises have been added and basic theory has been expanded. General Markov dependent sequences and their convergence to equilibrium is the subject of an entirely new chapter. The introduction of conditional expectation and conditional probability very early in the text maintains the pedagogic innovation of the first edition; conditional expectation is illustrated in detail in the context of an expanded treatment of martingales, the Markov property, and the strong Markov property. Weak convergence of probabilities on metric spaces and Brownian motion are two topics to highlight. A selection of large deviation and/or concentration inequalities ranging from those of Chebyshev, Cramer-Chernoff, Bahadur-Rao, to Hoeffding have been added,with illustrative comparisons of their use in practice. This also includes a treatment of the Berry-Esseen error estimate in the central limit theorem.The authors assume mathematical maturity at a graduate level; otherwise the book is suitable for students with varying levels of background in analysis and measure theory. For the reader who needs refreshers, theorems from analysis and measure theory used in the main text are provided in comprehensive appendices, along with their proofs, for ease of reference.Rabi Bhattacharya is Professor of Mathematics at the University of Arizona. Edward Waymire is Professor of Mathematics at Oregon State University. Both authors have co-authored numerous books, including a series of four upcoming graduate textbooks in stochastic processes with applications.
A BAYESIAN ANALYSIS OF CHANGING TIME SERIES MODELS
A Bayesian Network framework for probabilistic identification
A behavioural approach to financial portfolio selection problem
A Bivariate Pareto Distribution for Modeling Load Sharing Dependence
A Brief Introduction to Numerical Analysis
Probably I ought to explain why one more book on numerical methods can be useful. Without any doubt, there are many quite good and excellent books on the subject. But I know definitely that I did not realize this when I was a student. In this book, my first desire was to present those lectures that I wished I would have heard when I was a student. Besides, in spite of the profusion of textbooks, introductory courses, and monographs on numerical methods, some of them are too elementary, some are too difficult, some are far too overwhelmedwith applications, and most of them are too lengthy for those who want to see the whole picture in a short time. I hope that the brevity of the course left me no chance to obscure the beauty and depth of mathematical ideas behind the theory and methods of numerical analysis. I am convincedthat such a book should be very conciseindeed. It should be thoroughly structured, giving information in short sections which, ideally, are a half-page in length. Equally important, the book should not give an impression that nothing is left to work on in this field. Any time it becomes possible to say something about modern development and recent results, I do try to find time and place for this.
A Celebration of Mathematical Modeling
A Celebration of Mathematical Modeling
A Chronicle of Permutation Statistical Methods
A Chronicle of Permutation Statistical Methods
A Class of Optimization Problems
Mathematical Programming is concerned with the determination of a minimum or maximum of a function of several variables, which are required to satisfy a number of constraints. Such solutions are sought are sought in diverse fields, including Engineering, Operations Research, Management Science and Economics. Often these situations are mathematical representations of certain real world problems, and hence are turned as mathematical programming problems.
A Comparison of the Bayesian and Frequentist Approaches to Estimation
The main theme of this monograph is "comparative statistical inference. " While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: "which - proach should one use in a given problem?" It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
A Comparison of the Bayesian and Frequentist Approaches to Estimation
The main theme of this monograph is ¿comparative statistical inference. ¿ While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: ¿which - proach should one use in a given problem?¿ It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
A comprehensive guide to Bayesian CNN with variational inference
A Comprehensive Guide to Factorial Two-Level Experimentation
Factorial designs enable researchers to experiment with many factors. The 50 published examples re-analyzed in this guide attest to the prolific use of two-level factorial designs. As a testimony to this universal applicability, the examples come from diverse fields: Analytical Chemistry, Animal Science, Automotive Manufacturing, Ceramics and Coatings, Chromatography, Electroplating, Food Technology, Injection Molding, Marketing, Microarray Processing, Modeling and Neural Networks, Organic Chemistry, Product Testing, Quality Improvement, Semiconductor Manufacturing, and Transportation.Focusing on factorial experimentation with two-level factors makes this book unique, allowing the only comprehensive coverage of two-level design construction and analysis. Furthermore, since two-level factorial experiments are easily analyzed using multiple regression models, this focus on two-level designs makes the material understandable to a wide audience. This book is accessible to non-statisticians having a grasp of least squares estimation for multiple regression and exposure to analysis of variance. "This book contains a wealth of information, including recent results on the design of two-level factorials and various aspects of analysis¿ The examples are particularly clear and insightful." (William Notz, Ohio State University)"One of the strongest points of this book for an audience of practitioners is the excellent collection of published experiments, some of which didn¿t ¿come out¿ as expected¿ A statistically literate non-statistician who deals with experimental design will have plenty of motivation to read this book, and the payback for the effort will be substantial." (Max Morris, Iowa State University)
A Comprehensive Textbook on Sample Surveys
A Comprehensive Textbook on Sample Surveys
A Compression Technique for Non-Stationary Signals
In this book, we present a compression technique for non stationary signals such as Electroencephalography (EEG). We show that 90% compression is possible achieving very low reconstruction error. We show that the reconstructed compressed signals are suitable to use in applications such as seizure detection and Brain Computer Interface. We show a preliminary comparison of performance between the uncompressed and compressed signals of such applications. If you have any questions please contact the author directly. The code is available upon request.
A Concise Guide to Statistics
The text gives a concise introduction into fundamental concepts in statistics. Chapter 1: Short exposition of probability theory, using generic examples. Chapter 2: Estimation in theory and practice, using biologically motivated examples. Maximum-likelihood estimation in covered, including Fisher information and power computations. Methods for calculating confidence intervals and robust alternatives to standard estimators are given. Chapter 3: Hypothesis testing with emphasis on concepts, particularly type-I , type-II errors, and interpreting test results. Several examples are provided. T-tests are used throughout, followed important other tests and robust/nonparametric alternatives. Multiple testing is discussed in more depth, and combination of independent tests is explained. Chapter 4: Linear regression, with computations solely based on R. Multiple group comparisons with ANOVA are covered together with linear contrasts, again using R for computations.
A Contribution to Adaptive Randomization Designs in Clinical Trials