Mathematical Statistics with Applications

Mathematical Statistics with Applications provides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the EM algorithms and Markov chain Monte Carlo methods. Prior probability or statistics knowledge is not required. ? Step-by-step procedure to solve real problems, making the topic more accessible ? Exercises blend theory and modern applications ? Practical, real-world chapter projects ? Provides an optional section in each chapter on using Minitab, SPSS and SAS commands ? Student solutions manual, instructors manual and data disk available

[1]  Phillip J. Ross,et al.  Taguchi Techniques For Quality Engineering: Loss Function, Orthogonal Experiments, Parameter And Tolerance Design , 1988 .

[2]  Erniel B. Barrios,et al.  Bootstrap Methods , 2011, International Encyclopedia of Statistical Science.

[3]  G. Reinsel,et al.  Introduction to Mathematical Statistics (4th ed.). , 1980 .

[4]  M. Traugott,et al.  Assessing the Accuracy of Polls and Surveys , 1986, Science.

[5]  M. Niknian,et al.  Randomization and Monte Carlo Methods in Biology , 1993 .

[6]  W. Mendenhall,et al.  A Second Course in Statistics: Regression Analysis , 1996 .

[7]  Peter Sprent,et al.  Data Driven Statistical Methods , 1997 .

[8]  Ronald P. Cody,et al.  Applied Statistics and the SAS Programming Language. , 1986 .

[9]  Edward W. Frees,et al.  Data Analysis Using Regression Models: The Business Perspective , 1995 .

[10]  M. Dertouzos,et al.  Made in America: Regaining the Productive Edge , 1989 .

[11]  Saeed Maghsoodloo.  The Exact Relation of Taguchi's Signal-to-Noise Ratio to His Quality Loss Function , 1990 .

[12]  M. F. Fuller,et al.  Practical Nonparametric Statistics; Nonparametric Statistical Inference , 1973 .

[13]  James W. Tankard.  The Statistical Pioneers , 1984 .

[14]  Sammy G. Shina.  THE SUCCESSFUL USE OF THE TAGUCHI METHOD TO INCREASE MANUFACTURING PROCESS CAPABILITY , 1991 .

[15]  Jongsuk Ruth Lee,et al.  A Survey of Confidence Interval Formulae for Coverage Analysis , 1998 .

[16]  Lance B. Bush,et al.  Preliminary structural design of a lunar transfer vehicle aerobrake. M.S. Thesis , 1992 .

[17]  A. Edwards,et al.  A History of Probability and Statistics and Their Applications before 1750 , 1992 .

[18]  N. Balakrishnan,et al.  A Primer on Statistical Distributions , 2003 .

[19]  Mitchell J. Mergenthaler.  Nonparametrics: Statistical Methods Based on Ranks , 1979 .

[20]  Morton B. Brown,et al.  Robust Tests for the Equality of Variances , 1974 .

[21]  Sven Eriksson.  PLANNING PHARMACEUTICAL CLINICAL TRIALS. BASIC STATISTICAL PRINCIPLES. , 1996 .

[22]  R. Roy.  A Primer on the Taguchi Method , 1990 .

[23]  Teri A. Crosby,et al.  How to Detect and Handle Outliers , 1993 .

[24]  田口 玄一,et al.  System of experimental design : engineering methods to optimize quality and minimize costs , 1987 .

[25]  A. Madansky.  Prescriptions for working statisticians , 1988 .

[26]  Patrick Dattalo,et al.  Statistical Power Analysis , 2008 .

[27]  C. L. Chiang.  Statistical Methods of Analysis , 2003 .

[28]  Kenneth N. Berk,et al.  Data Analysis With Microsoft Excel , 2000 .

[29]  David Salsburg.  The Lady Tasting Tea , 2002 .

[30]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[31]  Fred Davidson.  Principles of statistical data handling , 1996 .

[32]  田口 玄一,et al.  Taguchi on robust technology development : bringing quality engineering upstream , 1993 .

[33]  Peter Dalgaard,et al.  Introductory statistics with R , 2002, Statistics and computing.

[34]  G. C. Wei,et al.  A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .

[35]  Roxy Peck,et al.  Statistics: A Guide to the Unknown , 2005 .

[36]  D. Wolfe,et al.  Nonparametric Statistical Methods. , 1974 .

[37]  Bhai Balmukand,et al.  The estimation of linkage from the offspring of selfed heterozygotes , 1928, Journal of Genetics.

[38]  Philippe Flajolet,et al.  Adaptive Sampling , 1997 .

[39]  Barrie Dale,et al.  APPLYING TAGUCHI METHODS TO THE DESIGN IMPROVEMENT PROCESS OF TURBOCHARGERS , 1991 .

[40]  Howard Thomas,et al.  Statistical Analysis for Business and Economics , 1967 .

[41]  田口 玄一,et al.  Introduction to quality engineering : designing quality into products and processes , 1986 .

[42]  Stephen L. Morgan,et al.  Sequential Simplex Optimization: A Technique for Improving Quality and Productivity in Research, Development, and Manufacturing , 1991 .

[43]  Karen A. F. Copeland.  Reliability Improvement with Design of Experiments , 1994 .

[44]  L. Tippett.  Statistical Tables: For Biological, Agricultural and Medical Research , 1954 .

[45]  O. L. Davies,et al.  Design and analysis of industrial experiments , 1954 .

[46]  C. J. Gilmore.  Chemometrics—applications of mathematics and Statistics to laboratory systems : R. G. Brereton, Ellis Horwood, Chichester, 1990. Pages 307. £45.00. , 1991 .

[47]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[48]  S. Raudenbush.  Statistical analysis and optimal design for cluster randomized trials , 1997 .

[49]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[51]  G. K. Robinson.  Practical Strategies for Experimenting , 2000 .

[52]  T. Porter.  The Rise of Statistical Thinking, 1820-1900 , 2020 .

[53]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[54]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[55]  M. Kendall.  Probability and Statistical Inference , 1956, Nature.

[56]  Michael R. Harwell.  Choosing Between Parametric and Nonparametric Tests , 1988 .

[57]  J. Kiefer.  Optimum Experimental Designs , 1959 .

[58]  Tommy Wright,et al.  On the Maximum Ratio: A Tool for Assisting Inaccuracy Assessment , 1983 .

[59]  William J. Zimmer,et al.  Tables of Sample Sizes in the Analysis of Variance , 1970 .

[60]  Genichi Taguchi,et al.  Taguchi methods : design of experiments , 1993 .

[61]  J. S. Hunter,et al.  Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. , 1979 .

[62]  J. Shao,et al.  The jackknife and bootstrap , 1996 .

[63]  Daniel C. St. Clair,et al.  Using Taguchi's method of experimental design to control errors in layered perceptrons , 1995, IEEE Trans. Neural Networks.

[64]  D. Heisey,et al.  The Abuse of Power , 2001 .

[65]  Robert L. Mason,et al.  Taguchi Methods: A Hands-On Approach , 1994 .

[66]  B. Cobb,et al.  A simple procedure for optimising the polymerase chain reaction (PCR) using modified Taguchi methods. , 1994, Nucleic acids research.

[67]  E. Jaynes.  Foundations of Probability Theory and Statistical Mechanics , 1967 .

[68]  R. Kuehl.  Design of Experiments: Statistical Principles of Research Design and Analysis , 1999 .

[69]  A. Winsor.  Sampling techniques. , 2000, Nursing times.

[70]  Rory A. Fisher,et al.  The Arrangement of Field Experiments , 1992 .

[71]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .

[72]  Hung T. Nguyen,et al.  Fundamentals of mathematical statistics , 1989 .

[73]  M. M. Kilgo,et al.  Statistics and Data Analysis: From Elementary to Intermediate , 2001 .

[74]  J. H. Zar,et al.  Biostatistical Analysis, 3rd edn. , 1996 .

[75]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[76]  E. S. Pearson.  "Student" as Statistician , 1939 .

[77]  G. Geoffrey Vining,et al.  Taguchi's parameter design: a panel discussion , 1992 .

[78]  Mark C. K. Yang,et al.  Understanding and Learning Statistics by Computer , 1986, World Scientific Series in Computer Science.

[79]  Russell V. Lenth,et al.  Some Practical Guidelines for Effective Sample Size Determination , 2001 .

[80]  David J. Hand,et al.  A Handbook of Small Data Sets , 1993 .

[81]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[82]  Anthony C. Atkinson,et al.  Recent Developments in the Methods of Optimum and Related Experimental Designs , 1988 .

[83]  J. V. Grice,et al.  A data-based approach to statistics , 1994 .