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These cookies do not store any personal information. Be aware though that the most interesting work in this area has arguably been done in the past decade, and hence is not covered by the book. Bayesian Nonparametrics Series Number The generalization to arbitrary random variables, as well as the interpretation bqyesian the set of exchangeable measures as a convex polytope, is due to: On a class of Bayesian nonparametric estimates.
The Best Books of If a random discrete measure is represented as a point process, its posterior is represented by a Palm measure. Fuzione caratteristica di un fenomeno aleatorio. The construction of models which do not nonparametricd such representations is a bit more demanding.
One categorical covariate Appendix A: Notation 4. One quantitative covariate Appendix B: Use of logarithms 5. Multiple regression, the linear predictor Appendix C: Some recommendations 6. Model building: from purpose to conclusion Appendix D: Programming in R, SAS and Stata Readership: Researchers using statistics in fields such as medicine, public health, dentistry, agriculture, and so on.
This specific orientation is emphasized as early as page 3 where three examples are discussed, one with a quantitative response, one with a binary response, and one with a survival time response. This book is extremely well written and the excellent diagrams produced by Therese Graversen enhance it.
The five page section 1. In summary, this book is excellent and fully appropriate for the target audience. Hughes, Thomas P. Errors in the physical sciences 6. Least-squares fitting of complex functions 2.
Random errors in measurement 7. Computer minimisation and the error matrix 3. Uncertainties as probabilities 8. Hypothesis testing—how good are our models 4. Error propagation 9. Topics for further summary 5. Data visualisation and reduction Readership: undergraduates in the physical sciences and engineering, graduate students, and professional scientists and engineers. I am really pleased to see this book. It has always troubled me that physics texts almost never include any actual data.
They seem to create a chasm between the packaged perfection of the theory and the fact that, at some point, those theories had been derived from data, with all its messiness. Life is never really that simple, and making no nod towards the path by which the final theory had been reached seems to me to give a misleading impression of what science is all about.
This book focuses squarely on the problems of moving from data to theory. It drags the treatment of uncertainties for practical physics courses into the twenty-first century. That means it assumes that the computer will do the number crunching, and that calculus-based approximations to errors and their propagation are replaced by a functional approach based on the use of standard packages such as spreadsheets.
The aim was to make it sufficiently user-friendly that students would actually take it into the laboratory, and I think the authors have succeeded. The level is suitable for undergraduates right through to graduate level. In Section 1. That reminded me of the time that I was on a flight to New York, which ran out of fuel and was forced to land at a military airbase.
I wonder how often this happens. I have only one very minor quibble: there appears to be no discussion of digit preference or heaping of data, though this is quite a common phenomenon with analogue measuring instruments.
Overall, however, this is a rather beautiful little book. David J. Hand: d. Algebraic and geometric methods in statistics Part III. Non-parametric estimation Raymond F. Rogantin, Henry P. Wynn Streater Part I. Contingency Tables Banach manifold of quantum states Raymond 2. Maximum likelihood estimation in latent class F.
Streater models Stephen E. Fienberg, Patricia Hersh, Axiomatic geometries for text documents tables Aleksandra B. Guy Lebanon Fienberg Exponential manifold by reproducing kernel 4. Extended exponential models Daniele 5. Dinwoodie, Ruriko Yoshida Quantum statistics and measures of quantum 6. Information Geometry and Algebraic 7. Algebraic varieties vs differentiable manifolds 8. Fienberg Part V. Designed Experiments Coloured figures for Chapter 2 9.
Generalised design Hugo Maruri-Aguilar, Maximum likelihood estimation in latent class Henry P. Wynn models Yi Zhou Design of experiments and biochemical network Fienberg Stigler Indicator function and sudoku designs Roberto Replicated measurements and algebraic Extended exponential models Daniele Markov basis for design of experiments and Imparato, Barbara Trivellato three-level factors Satoshi Aoki, Akimichi Takemura Readership: The book is meant for mathematical statisticians and mathematicians interested in relatively recent applications of computational commutative algebra and differential geometry to Statistics, specifically to categorical data, design of experiments, and classical and quantum information geometry.
Sophisticated algebraic methods were introduced in Statistics by Wijsman s , Linnik, and Kagan a bit later to study similar regions and best unbiased estimators for families of densities that are not complete, specifically for algebraic exponential families.
These are curved exponential families where the parametric space is defined by algebraic equations in the natural parameters. A famous example is the common mean problem for two normals with possibly different variances.
A beautiful but somewhat esoteric result was the Kagan—Palamadov theorem characterizing all best unbiased estimators in such cases. This settled a conjecture of the present reviewer. The recent resurgence of algebraic methods addresses equally hard but more realistic problems in contingency tables, where models are often described through polynomial equations. A famous example of use of algebraic methods in an important problem is the Diaconis—Sturmfels paper on algebraic algorithms for sampling from conditional distributions for contingency tables.
This helped in constructing conditional tests. The construction was very sophisticated, using Gromov bases. Incidentally, Gromov is a recent recipient of the Abel Prize in Mathematics. Algebraic Geometry has been used by Feinberg and a few others for geometric representation for contingency tables or finding the mle in latent class models for categorical variables. Finding the mle in these popular models is surprisingly difficult.
Apparently users are not aware, so sometimes EM and other methods are applied without proper justification. The discrete latent class models arose from the pioneering work of Goodman, Haberman, and others as models for joint marginals for manifest variables, which are conditionally independent given an unobservable latent variable.
These applications are discussed in Part 1. For me this was the most interesting part of the book. However algebraic techniques, including Groebner bases, have also been applied to design of experiments by Maruri-Aguilar, Aoki, Takemura, etc. The paper by the last authors on fractional factorials is quite interesting.
Part 3 introduces information geometry. Statisticians will recognize the well-known contri- butions of Efron relating curvature and C. Part 3 also introduces a quantum information theory. I missed any reference to the work of Professor K. Parthasarathy, the very well-known probabilist, analyst, and quantum probabilist at ISI, Delhi. He has a beautiful recent book on quantum Information theory, published by Hindustan Book Agency, India.
I see many highly non-trivial applications to hard statistical problems presented with great care and love, if I may use that word. But I also note the cautionary advice given by Feinberg et al. It applies not only to the latent class models, as meant by them, but to all the very hard applications of Algebra and Geometry. Use these methods with care, all the problems are hard!
I would also draw the attention of the interested reader to the work of Drton on algebraic exponential families, with possible singularities, especially for popular latent variable models like Factor Analysis.
Some of his papers and books are listed in the book. Jayanta K. Ghosh: ghosh stat. The volume contains 50 original papers, a chronological listing of all publications, as well as individual commentary on particular facets of the research by each of the editors. Remarkably it also contains a short overview by Chris Heyde himself, just prior to his death, of what he regarded as some of his key contributions over his lifetime.
Chris Heyde published over scholarly articles. Some of his principal contributions were in the fields of: Probability Theory: Rates of convergence in the Central Limit Theorem and the Martingale Central Limit Theorem, Law of the Iterated Logarithm, Branching Processes, Population Genetics Stochastic processes: Inference using Quasi-Likelihood and Asymptotic Quasi-Likelihood, Parameter estimation for random processes with long-range dependence Modelling in financial markets: Risky asset modelling; need for fractal properties and heavy tails for risky asset returns.
It is still arguably the most useful model for capturing empirical realities of stock and stock index returns. Roger Gay: roger. Introduction to R 5.
Regression generalizations 2. Data management 6. Graphics 3. Common statistical procedures 7. Advanced applications 4. R is an open source package to perform statistical and graphical tasks at all levels. R has the advantage that it is an open programming environment and offers interesting applications and user contributed packages in CRAN for a large variety of users.
A potentially difficult task is the data preparation and management that foregoes all statistical analyses and can take almost the same time and therefore the book emphasizes this aspect as it is stated in the title. The last decade has seen many textbooks that introduces to the R language at all levels. There are essentially approaches: one that emphasize the statistical theory part and give R examples and book that emphasize the computational side and develops R as statistical software.
This is a book that is written in the latter spirit but is new insofar as it incorporates also the knowledge of 40 contributed packages. Given the increasing number of contributed packages in R in CRAN comprehensive R archive network it might be conceivable that we will see more books of this type in the future.
Our primary goal is to provide users with an easy way to learn how to perform an analytic task in [R]. We include many common tasks, including data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics. Introductory R books are geared mainly to students and researchers making statistical applications.
The book contains a detailed subject index and an R command index, describing the R syntax. In addition to the HELP examples, there are extended examples and case studies that demonstrate R codes and facilitate the searches for hints by the reader. An extensive index helps the reader to find the relevant commands to perform the desired analysis.
It would be nice if a book like this one could be made available online, so that the search costs are becoming smaller. For example, to change a plot symbol in a scatterplot, most occasional users will have to check the manual or look up the manual or a book. Hopefully future edition of R will come with a menu on the screen to avoid such time consuming search costs.
The book shows the technical possibilities of R to display results of a statistical analysis by graphs. Chapter 1 introduces to R and Chapter 2 to the data management capabilities. The interesting aspect of the book is, that it does not only describe the basic statistics and graphics function of the basic R system but it describes the use of 40 additional available from the CRAN website.
The website contains also the R code to install all the packages that contain the described features. In summary, the book is a useful complement to introductory statistics books and lectures, but cannot be used as a standalone introductory statistics text book. Readers should be familiar with the concept of statistics and should have some experience as what can be done with statistics.
Those who know R might get additional hints on new features of statistical analyses. Wolfgang Polasek: polasek ihs. Prologue Correlated data 2. Fundamental ideas I Count data 3. Integration versus simulation Time to event data 4.
Fundamental ideas II Binary diagnostic tests 5. Comparing populations Nonparametric models 6. Simulations Appendix A: Matrices and vectors 7. Basic concepts of regression Appendix B: Probability 8. Binomial regression Appendix C: Getting started in R 9. Linear regression Readership: MS and PhD students of statistics, biostatistics, epidemiology and other areas of science. This is a modern book on Bayesian statistics, so that as well as outlining the philosophical core of Bayesian inference it includes discussion and examples of how to apply such methods in practice, using both WinBUGS and R.
Certain average-weighted versions, called AFIC, allowing several focus parameters to be nips simultaneously, are also developed. Optimal inference via confidence distributions for two-by-two tables modelled as Poisson pairs.
Classical test theory applications of R in item response modeling 6. The book consists of three chapters: Introduction to social bxyesian research principles Needless to say, the revised volume obviously continues as a celebrated classic.
One categorical covariate Appendix A: They begin with one or two examples, for which numerical data or geographical figures are presented. This is achieved by comparing the mean squared error for estimating a focus parameter under consideration, for each candidate model.
Looking for beautiful books? In particular this yields FIC formulae for covariances or correlations at specified lags, for the probability of reaching a threshold, etc. What price semiparametric Cox regression?. The methods described in the book can be applied using R libraries hort functions made available also by the authors. These are covered in every textbook on probability theory. Permanent link to this document https: In his acknowledgments, Ferguson attributes the idea to David Blackwell.
Annals of Statistics, 34 2: Transactions of the American Mathematical Society, 80 2: However, this book is probably not a good place to start if you do not already have a reasonable knowledge of the field. Lecture notes Video tutorials: Annals of Statistics, 12 1: In applications, these models are typically used as priors on the mixing measure of a mixture model e.
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