Meullenetm, J.-F. et al.
Multivariate and Probabilistic Analyses of Sensory Science Problems
Blackwell Science 2007.7
256 pp. (H)
ISBN 0-8138-0178-8
24,400円
Contents
Foreword / Introduction / 1: A description of sample datasets / 2: Panelist and Panel Performance a Multivariate Experience / 3: A Non-Technical Description of Preference Mapping / 4: Deterministic extensions to preference mapping techniques / 5: Multidimensional scaling and unfolding and the application of probabilistic unfolding to model preference data / 6: Consumer Segmentation Techniques / 7: Ordinal Logistic Regression Models in Consumer Research / 8: Risk assessment in sensory and consumer science / 9: Application of MARS to Preference Mapping / 10: Analysis of Just About Right data / Index / *
* Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration of consumer data, including segmentation and identifying drivers of liking. *
Rencher, A. C. & Schaalje,B. G.
Linear Models in Statistics 2nd ed.
統計学の線形モデル 第2版
John Wiley & Sons 2008.1
672 pp.(H)
ISBN 0-471-75498-6
14,800円
Contents
1. Introduction/ 2. Matrix Algebra/ 3. Random Vectors and Matrices/ 4. Multivariate Normal Distribution/ 5. Distribution of Quadratic Forms in y/ 6. Simple Linear Regression/ 7. Multiple Regression: Estimation/ 8. Multiple Regression: tests of Hypotheses and Confidence Intervals/ 9. Multiple Regression: Model Validation and Diagnostics/ 10. Multiple Regression: random x's/ 11. Multiple Regression: Bayesian Inference/ 12. Analysis-of-Variance Models/ 13. One-Way Analysis-of-Variance: balanced Case/ 14. Two-Way Analysis-of Variance: Balanced Case/ 15. Analysis-of-Variance: The Cell Means Model for Unbalanced Data/ 16. Analysis-of-Covariance/ 17. Linear Mixed Models/ 18. Additional Models/ Appendix A. Answers and Hits to the Problems/ References/ Index/
*Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. *
Seber, G. A. F.
A Matrix Handbook for Statisticians
統計者のためのマトリクスハンドブック
John Wiley & Sons 2007.11
559 pp. (H)
ISBN 0-471-74869-2
13,200円
Contents
1. Notation/ 2. Vectors, Vector Spaces, and Convexity/ 3. Rank/ 4. Matrix Functions: Inverse, Transpose, Trace, Determinant, and Norm/ 5. Complex, Hermitian, and Related Matrices/ 6. Eigenvalues, Eigenvectors, and Singular Values/ 7. Generalized Inverses/ 8. Some Special Matrices/ 9. Non-Negative Vectors and Matrices/ 10. Positive Definite and Non-negative Definite Matrices/ 11. Special Products and Operators/ 12. Inequalities/ 13. Linear Equations/ 14. Partitioned Matrices/ 15. Patterned Matrices/ 16. Factorization of Matrices/ 17. Differentiation and Finite Differences/ 18. Jacobians/ 19. Matrix Limits, Sequences and Series/ 20. Random Vectors/ 21. Random Matrices/ 22. Inequalities for Probabilities and Random Variables/ 23. Majorization/ 24. Optimization and Matrix Approximation/ References/ Index/
* This book emphasizes computational statistics and algorithms and includes numerous references to both the theory behind the methods and the applications of the methods. Each chapter consists of four parts: a definition followed by a list of results, a short list of references to related topics in the book (since some overlap is unavoidable), one or more references to proofs, and references to applications. *
Bartoszynski,R. & Niewiadomska-Bugaj,M.
Probability and Statistical Inference 2nd ed.
確率と統計的インターフェイス 第２版
John Wiley & Sons 2008.1
647 pp.(H)
ISBN 0-471-69693-5
14,800円
Contents
1. Experiments, Sample Spaces, and Events/ 2. Probability/ 3. Counting/ 4. Conditional Probability; Independence/ 5. Markov Chains*/ 6. Random Variables: Univariate Case/ 7. Random Variables: Multivariate Case/ 8. Expectation/ 9. Selected Families of Distributions/ 10. Random Samples/ 11. Introduction to Statistical Inference/ 12. Estimation/ 13. Testing Statistical Hypotheses/ 14. Linear Models/ 15. Rank Methods/ 16. Analysis of Categorical Data/ Statistical Tables/ Bibliography/ Answers to Odd-Numbered Problems/ Index/
*Probability and Statistical Inference, Second Edition is a user-friendly book that stresses the comprehension of concepts instead of the simple acquisition of a skill or tool. It provides a mathematical framework that permits students to carry out various procedures using any number of computer software packages as opposed to relying on one particular package. Its unique approach to problems allows readers to integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic. *
Madsen, H.
Time Series Analysis
Taylor & Francis 2007.12
400 pp.(H)
ISBN 1-4200-5967-X
9,700円
Contents
1.Preface/ 2.Introduction/ 3.Multivariate Random Variables/ 4.Regression-Based Methods/ 5.Linear Dynamic Systems/ 6.Stochastic Processes/ 7.Identification, Estimation, and Model Checking/ 8.Spectral Analysis/ 9.Linear Systems and Stochastic Processes/ 10.Multivariate Time Series/ 11.State Space Models of Dynamic Systems/ 12.Recursive Estimation/ 13.Real Life Inspired Problems/ 14.Appendices/ 15.Bibliography/ Index/
* With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. Emphasizing the time domain description, the author presents theorems to highlight the most important results, proofs to clarify some results, and problems to illustrate the use of the results for modeling real-life phenomena. *
Bernardo, J.M. et al. ed.
Bayesian Statistics 8
Oxford U.P. 2007.7
688 pp. (H)
ISBN 0-19-921465-4
27,200円
Contents
1 Generative or Discriminative? Getting the Best of Both Worlds / 2 Assessing the Effect of Genetic Mutation - A Bayesian Framework for Determining Population History from DNA Sequence Data / 3 Some Aspects of Bayesian Model Selection for Prediction / 4 Nonparametric Function Estimation Using Overcomplete Dictionaries / 5 Sequential Monte Carlo for Bayesian Computation / 6 Dynamic Gaussian Process Priors, with Applications to The Analysis of Space-time Data / 7 Bayesian Nonparametric Modelling for Spatial Data Using Dirichlet Processes / 8 Bayesian Nonparametric Latent Feature Models / 9 Objective Bayesian Analysis of Multiple Changepoints for Linear Models / 10 Bayesian Relaxation: Boosting / 11 The Bayesian Approach to the Analysis of Finite Population Surveys / 12 Detecting selection in DNA sequences: Bayesian Modelling and Inference / 13 Deriving Bayesian and frequentist estimators from time-invariance estimating equations: a unifying approach / 14 FDR and Bayesian Multiple Comparisons Rules / 15 Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity / 16 Approximating Interval Hypothesis: p-values and Bayes Factors / 17 Bayesian Probability in Quantum Mechanics / 18 Fast Bayesian Shape Matching Using Geometric Algorithms / 19 Nested Sampling for Bayesian Computations / 20 Objective Bayesian Analysis for the Multivariate Normal Model / *
Zuur, Alain F. et al.
Analysing Ecological Data
Springer-Verlag 2007.
672 pp. (H)
ISBN 0-387-45967-7
11,200円
Contents
Contributors / 1 Introduction / 2 Data management and software / 3 Advice for teachers / 4 Exploration / 5 Linear regression / 6 Generalised linear modelling / 7 Additive and generalised additive modelling / 8 Introduction to mixed modelling / 9 Univariate tree models / 10 Measures of association / 11 Ordination ― First encounter / 12 Principal component analysis and redundancy analysis / 13 Correspondence analysis and canonical correspondence analysis / 14 Introduction to discriminant analysis / 15 Principal coordinate analysis and non-metric multidimensional scaling / 16 Time series analysis ― Introduction / 17 Common trends and sudden changes / 18 Analysis and modelling of lattice data / 19 Spatially continuous data analysis and modelling / 20 Univariate methods to analyse abundance of decapod larvae / 21 Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal / 22 Crop pollination by honeybees in Argentina using additive mixed modelling / 23 Investigating the effects of rice farming on aquatic birds with mixed modelling / 24 Classification trees and radar detection of birds for North Sea wind farms / 25 Fish stock identification through neural network analysis of parasite fauna / 26 Monitoring for change: Using generalised least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands / 27 Univariate and multivariate analysis applied on a Dutch sandy beach community / 28 Multivariate analyses of South-American zoobenthic species ― spoilt for choice / 29 Principal component analysis applied to harbour porpoise fatty acid data / 30 Multivariate analyses of morphometric turtle data ― size and shape / 31 Redundancy analysis and additive modelling applied on savanna tree data / 32 Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico / 33 Estimating common trends in Portuguese fisheries landings / 34 Common trends in demersal communities on the Newfoundland-Labrador Shelf / 35 Sea level change and salt marshes in the Wadden Sea: A time series analysis / 36 Time series analysis of Hawaiian waterbirds / 37 Spatial modelling of forest community features in the Volzhsko-Kamsky reserve / References / Index /
* This book provides a practical introduction to analysing ecological data using real data sets collected as part of postgraduate ecological studies or research projects. The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modelling techniques), multivariate analysis, time series analysis (e.g. common trends) and spatial statistics. The second part provides 17 case studies, mainly written together with biologists who attended courses given by the first authors. The case studies include topics ranging from terrestrial ecology to marine biology. The case studies can be used as a template for your own data analysis; just try to find a case study that matches your own ecological questions and data structure, and use this as starting point for you own analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in Chapter 2. *
653-9 登録日 08.03.22
