Introduction to Mathematical Statistics

Excellent Introduction to Mathematical Statistics Introduction to Mathematical Statistics by Robert Hogg and Allen Craig (First Edition through Fifth Edition) by Robert Hogg, Joseph McKean and Allen Craig (Sixth Edition) Publication History: First Edition * (Year 1959) * (245 pages) Second Edition * (Year 1965) * (383 pages) Third Edition * (Year 1970) * (415 pages) Fourth Edition * (Year 1978) * (448 pages) * (ISBN-10: 0029789907) * (ISBN-13: 978-0029789902) * (ISBN-10: 0023557109) * (ISBN-13: 978-0023557101) Fifth Edition * (Year 1994) * (576 pages) * (ISBN-10: 0023557222) * (ISBN-13: 978-0023557224) Sixth Edition * (Year 2004) * (692 pages) * (ISBN-10: 0130085073) * (ISBN-13: 978-0130085078) I read the third edition when it was first published. I was in school; I used it in an undergraduate class. Since then "Hogg and Craig" and now "Hogg; McKean and Craig" has been a regular reference work for me. The only editions I've only read completely are the third edition and the sixth edition; I have used the fifth edition as a reference source since it was published first published. The coverage of mathematical statistics is concise and very thorough in all the editions. The chapter on "Sufficient Statistics" is the clearest presentation I have ever seen. The chapter "Theory of Statistical Tests" is a very advanced treatment but very easy to use for any practitioner. Another of my favorites is the last chapter on "Non-Parametric Statistics." The following are good backup references and books that expand on ideas covered in "Introduction to Mathematical Statistics." Other references on mathematical Statistics: * (Mathematical Methods of Statistics. (PMS-9)) * (Introduction to Probability Theory and Statistical Inference (Wiley Series in Probability and Mathematical Statistics. Probability and Mathematical Statistics)) * (Random Variables and Probability Distributions (Cambridge Tracts in Mathematics)) Probability Theory * (An Introduction to Probability Theory and its Applications: Volume I (Third Edition)) * (An Introduction to Probability Theory and Its Applications, Volume 2) The Linear Model * (Linear Models for Multivariate, Time Series, and Spatial Data (Springer Texts in Statistics)) * (Regression Analysis: Theory, Methods, and Applications (Springer Texts in Statistics)) Non-Parametric Models * (All of Nonparametric Statistics (Springer Texts in Statistics)) * (Nonparametric Statistical Methods, 2nd Edition) * ([[ASIN:B000VI5U4W Analyzing Categorical Data (Springer Texts in Statistics)(Wiley Series in Probability and Statistics)]]) I've enjoyed this book since I first read it in 1971. The new edition is every bit as practical and enjoyable as were the older editions. I recommend it to others with no reservations.

Excellent Introduction to Mathematical Statistics Introduction to Mathematical Statistics by Robert Hogg and Allen Craig (First Edition through Fifth Edition) by Robert Hogg, Joseph McKean and Allen Craig (Sixth Edition) Publication History: First Edition * (Year 1959) * (245 pages) Second Edition * (Year 1965) * (383 pages) Third Edition * (Year 1970) * (415 pages) Fourth Edition * (Year 1978) * (448 pages) * (ISBN-10: 0029789907) * (ISBN-13: 978-0029789902) * (ISBN-10: 0023557109) * (ISBN-13: 978-0023557101) Fifth Edition * (Year 1994) * (576 pages) * (ISBN-10: 0023557222) * (ISBN-13: 978-0023557224) Sixth Edition * (Year 2004) * (692 pages) * (ISBN-10: 0130085073) * (ISBN-13: 978-0130085078) I read the third edition when it was first published. I was in school; I used it in an undergraduate class. Since then "Hogg and Craig" and now "Hogg; McKean and Craig" has been a regular reference work for me. The only editions I've only read completely are the third edition and the sixth edition; I have used the fifth edition as a reference source since it was published first published. The coverage of mathematical statistics is concise and very thorough in all the editions. The chapter on "Sufficient Statistics" is the clearest presentation I have ever seen. The chapter "Theory of Statistical Tests" is a very advanced treatment but very easy to use for any practitioner. Another of my favorites is the last chapter on "Non-Parametric Statistics." The following are good backup references and books that expand on ideas covered in "Introduction to Mathematical Statistics." Other references on mathematical Statistics: * (Mathematical Methods of Statistics. (PMS-9)) * (Introduction to Probability Theory and Statistical Inference (Wiley Series in Probability and Mathematical Statistics. Probability and Mathematical Statistics)) * (Random Variables and Probability Distributions (Cambridge Tracts in Mathematics)) Probability Theory * (An Introduction to Probability Theory and its Applications: Volume I (Third Edition)) * (An Introduction to Probability Theory and Its Applications, Volume 2) The Linear Model * (Linear Models for Multivariate, Time Series, and Spatial Data (Springer Texts in Statistics)) * (Regression Analysis: Theory, Methods, and Applications (Springer Texts in Statistics)) Non-Parametric Models * (All of Nonparametric Statistics (Springer Texts in Statistics)) * (Nonparametric Statistical Methods, 2nd Edition) * ([[ASIN:B000VI5U4W Analyzing Categorical Data (Springer Texts in Statistics)(Wiley Series in Probability and Statistics)]]) I've enjoyed this book since I first read it in 1971. The new edition is every bit as practical and enjoyable as the older editions. I recommend it to others with no reservations.

Hogg and Craig is one of my favorite texts. It is an intermediate text in mathematical statistics similar to Mood, Graybill and Boes. I took qualifying exams in mathematics for my Masters Degree at the University of Maryland in the early 1970s. One of the exams I took was in statistics. I had little formal training in statistics at the time. Hogg and Craig was the recommended text for the statistics exam. So I bought it and studied out of it on my own. It was very clear with excellent coverage of methods for deriving distributions for random variables and transformations of random variables. I passed my exams and got my highest grades on the statistics exam even though I had more training in abstract algebra. Hogg and Craig really helped. It has been revised since then to maintain currency with statistical developments but it still has maintained its clarity and usefulness. Most of the other reviews that are critical of it are way off base. I am sure that efforts have been made with the numerous revisions to keep the material up to date. Perhaps some critics are correct that it comes up short on some modern advances in Bayesian statistics and other computer-intensive statistical methods. But that should not tarnish its reputation as a classic text in mathematical statistics.

This is one of the most famous books on mathematical statistics. Is it good though? Many reviewers think not. My own personal opinion is that it is fantastic and extremely carefully written. The sixth edition has been completely modernized and includes such "modern" topics as MCMC, robust methods and the EM algorithm. If you approach this book with a view of getting a *first course in basic statistics*, you will be disappointed and find the book very difficult. Hence many reviewers' dislike of it. Perhaps the authors are to blame for this, by using the phrase "Introduction to..." in the title, when clearly the book is much more than a mere introduction. To their credit, the authors do warm in the Preface that the book is aimed at beginning graduate students. So here is the deal: if you are looking for a truly introductory book, try Clarke and Cooke's "Basic Statistics" or Mendenhall et al.'s "Mathematical Statistics with Applications." However, if you have some background in basic probability, statistics and calculus, then Hogg et al.'s book will be invaluable to you, because it is extremely well-written (by the very best in the field) and comprehensive. Together with Casella and Berger's Statistical Inference, it is one of the best existing books in intermediate to advanced statistics.

Hogg and Craig is one of my favorite texts. It is an intermediate text in mathematical statistics similar to Mood, Graybill and Boes. I took qualifying exams in mathematics for my Masters Degree in math at the University of Maryland in the early 1970s. One of the exams I took was in statistics. I had little formal training in statistics at the time. Hogg and Craig was the recommended text for the statistics exam. So I bought it and studied out of it on my own. It was very clear with excellent coverage of methods for deriving distributions for random variables and transformations of random variables. I passed my exams and got my highest grades on the statistics exam even though I had more training in abstract algebra. Hogg and Craig really helped. It has been revised since then to maintain currency with statistical developments but it still has maintained its clarity and usefulness.