Lesik, S.
Applied Statistical Inference with MINITAB
Chapman & Hall/ CRC, USA 2009.12
464 pp.(H)
ISBN 1-4200-6583-1
11,400円
Contents
1. Introduction/ 2. Graphing Variables/ 3. Descriptive Representations of Data and Random Variables/ 4. Basic Statistical Inference/ 5. Simple Linear Regression/ 6. More on Simple Linear Regression/ 7. Multiple Regression Analysis/ 8. More on Multiple Regression/ 9. Analysis of Variance (ANOVA)/ 10. Other Topics/ Index/
* Through clear, step-by-step mathematical calculations, Applied Statistical Inference with MINITAB enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis. *
Clarke, B. et al.
Principles and Theory for Data Mining and Machine Learning
Springer Series in Statistics
Springer-Verlag 2009.
786 pp. (H)
ISBN 0-387-98134-9
11,200円
Contents
1. Variability, information, prediction/ 2. Kernel smoothing/ 3. Spline smoothing/ 4. New wave nonparametrics/ 5. Supervised learning: Partition methods/ 6. Alternative nonparametrics/ 7. Computational comparisons/ 8. Unsupervised learning: Clustering/ 9. Learning in high dimensions/ 10. Variable selection/ 11. Multiple testing/ Index/ *
LeSage, J. & Pace, R. K.
Introduction to Spatial Econometrics
Chapman & Hall/ CRC, USA 2009.1
374 pp.(H)
ISBN 1-4200-6424-X
11,200円
Contents
1. Introduction/ 2. Motivating and Interpreting Spatial Econometric Models/ 3. Maximum Likelihood Estimation/ 4. Log-Determinants and Spatial Weights/ 5. Bayesian Spatial Econometric Models/ 6. Model Comparison/ 7. Spatiotemporal and Spatial Models/ 8. Spatial Econometric Interaction Models/ 9. Matrix Exponential Spatial Models/ 10. Limited Dependent Variable Spatial Models/ References/ Index/
* Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances. *
Tan, M. T. et al. ed.
Bayesian Missing Data Problems
EM, Data Augmentation and Noniterative Computation
Chapman & Hall 2009.8
344 pp.(H)
ISBN 1-4200-7749-X
11,400円
Contents
1. Introduction/ 2. Optimization, Monte Carlo Simulation and Numerical Integration/ 3. Exact Solutions/ 4. Discrete Missing Data Problems/ 5. Computing Posteriors in the EM-Type Structures/ 6. Constrained Parameter Problems/ 7. Checking Compatibility and Uniqueness/ Appendix/ References/ Indices/
* Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. *
Bain, A. & Crisan, D.
Fundamentals of Stochastic Filtering
Stochastic Modelling and Applied Probability Series
Springer-Verlag 2009.
390 pp. (H)
ISBN 0-387-76895-5
8,100円
Contents
1. Introduction/ 2. The Stochastic Process/ 3. The Filtering Equations/ 4. Uniqueness of the Solution to the Zakai and the Kushner-Stratonovitch Equations/ 5. Other results/ 6. Finite Dimensional Filters/ 7. The Density of the Conditional Distribution of the Signal/ 8. Numerical Methods for Solving the Filtering Problem/ 9. A Continuous Time Particle Filter/ 10. Particle Filters in Discrete Time/ 11. Measure Theory/ 12. Stochastic Analysis/ References/ Index/ *
Serfozo, R.
Basics of Applied Stochastic Processes
Probability and Its Applications Series
Springer-Verlag 2009.
443 pp. (H)
ISBN 3-540-89331-8
11,200円
Contents
1. MarkovChains/ 2. Renewal and Regenerative Processes/ 3. Poisson Processes/ 4. Continuous-Time Markov Chains/ 5. Brownian Motion/ 6. Appendix/ Bibliographical Notes/ References/ Notation/ Index/ *
Broman, K.W. &Sen, S.
A Guide to QTL Mapping with R/qtl
Statistics for Biology and Health Series
Springer-Verlag 2009.
400 pp. (H)
ISBN 0-387-92124-9
13,700円
Contents
1. Introduction/ 2. Importing and simulating data/ 3. Data checking/ 4. Single-QTL analysis/ 5. Non-normal phenotypes/ 6. Experimental design and power/ 7. Working with covariates/ 8. Two-dimensional, two-QTL scans/ 9. Fit and exploration of multiple-QTL models/ 10. Case study I/ 11. Case study II/ Index/ *
Feldman, R. M. & Valdez-Flores, C.
Applied Probability and Stochastic Processes 2nd ed.
応用確率と確率過程 第２版
Springer-Verlag 2010.2
397 pp.(H)
ISBN 3-642-05155-3
20,900円
Contents
1. Basic Probability Review/ 2. Basics of Monte Carlo Simulation/ 3. Basic Statistical Review/ 4. Poisson Processes/ 5. Markov Chains/ 6. Markov Processes/ 7. Queueing Processes/ 8. Queueing Networks/ 9. Event-Driven Simulation and Output Analyses/ 10. Inventory Theory/ 11. Replacement Theory/ 12. Markov Decision Processes/ 13. Advanced Queues/ A: Matrix Review/ References/ Index/
* This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with numerous examples and exercises to illustrate the range of engineering and science applications of the concepts. The book is designed to give the reader an intuitive understanding of probabilistic reasoning, in addition to an understanding of mathematical concepts and principles. *
677-67 登録日 10.02.13
