Reviews

Books reviewed in this article:
Common Errors in Statistics (and How to Avoid Them). 2d Edition Philip I. Good and James W. Hardin
Statistical thinking in busineess, 2nd Edition J Bobe E J A John, D Whitaker & D G Johnson
Mathematical Statistics with Applications. A S Kapadia, W Chan and L Moyé
The Cambridge Dictionary of Statistics. 3rd Edition. B. S. Everitt
The Oxford Dictionary of Statistical Terms. 6th edition Y. Dodge     

Book Reviews

Books reviewed:
Philip Hans Franses & Dick van Dijk, Non-linear Time Series Models in Empirical Finance
Herbert Spirer, Louise Spirer & A.J. Jaffe, Misused Statistics
Deborah J. Bennett, Randomness
C.E. Linneborg, Data Analysis by Resampling: Concepts and Applications
I. Clark and W.V. Harper, Practical Geostatistics 2000     

BOOK REVIEWS

Book reviewed in this article:
A Primer on the Taguchi Method. By Ranjit K. Roy.
Sample Size Choice: Charts for Experiments with Linear Models. By Robert E. Odeh & Martin Fox.
Interpretation and Uses of Medical Statistics. By Leslie E. Daly, GeoErey J. Bourke & James McGilvray.
Deming's 14 Points Applied To Services. By A.C. Rosander.     

Book Reviews

Books reviewed:
R.J. Adler, R.E. Feldman & M.S. Taqqu, A Practical Guide to Heavy Tails: Statistical Techniques and Applications.
J.J. Foste, A Beginner's Guide to Data Analysis Using SPSS for Windows.
N. Limnios and G. Oprisan, Semi–Markov Processes and Reliability.     

Book Reviews

Book reviewed in this article:
Probability Theory: Independence, Interchangeability, Martingales. By Y. S. Chow and H. Teicher.
Gaussian Random Processes. By I. A. Ibragimov & Y. A. Rozanov.
Stochastic Approximation Methods for Constrained and Unconstrained Systems. By Harold J. Kushner & Dean S. Clark.
Random Walks with Stationary Increments. By H. C. P. Berbee.
Asymptotic Optimality Theory for Testing Problems with Restricted Alternatives. By T. A. B. Snijders.
Large Deviation and Asymptotic Efficiencies. By P. Groeneboom.
Asymptotic Theory of Statistical Tests and Estimation. Edited by I. M. Chakravarti.
Developments in Statistics, Vol. 1. Edited by P. R. Krishnaiah.
Developments in Statistics, Vol. 2. Edited by P. R. Krishnaiah.
The Analysis of Cross-Classified Categorical Data. By S. E. Feinberg.
Analysis of Qualitative Data, Volume 2. By Shelby J. Haberman.
Introduction to Statistics. By M. R. Heyworth & J. R. Sealy.
Identification of Outliers. By D. M. Hawkins.
Randomization Tests. By Eugene S. Edgington.
Tables for Normal Tolerance Limits, Sampling Plans, and Screening. By Robert E. Odeh & D. B. Owen.
The Algebra of Econometrics. By D. S. G. Pollock.
Decision Theory and Social Ethics. Edited by Hans W. Gottinger and Werner Leinfellner.     

Inferences on partial linear models with right censored data by empirical likelihood

ABSTRACT

This article considers inference for partial linear models with right censored data. We use empirical likelihood based on the Buckley and James (1979 Buckley, J., James, I. (1979). Linear regression with censored data. Biometrika 66:429–436.[Crossref], [Web of Science ®] , [Google Scholar]) estimating equation to derive the confidence region for the regression parameter. We introduce an adjusted empirical likelihood ratio statistic for the parameter of interest and show that its limiting distribution is standard chi-square. A simulation is carried out to compare our method with the synthetic data approach in Wang and Li (2002 Wang, Q.-H., Li, G. (2002). Empirical Likelihood Semiparametric Regression Analysis under Random Censorship. J. Multivariate Anal. 83:469–486.[Crossref], [Web of Science ®] , [Google Scholar]).     

Robustness of the shrinkage estimator for the relative potency in the combination of multivariate bioassays

ABSTRACT

This article investigates the robustness of the shrinkage Bayesian estimator for the relative potency parameter in the combinations of multivariate bioassays proposed in Chen et al. (1999 Chen, D.G., Carter, E.M., Hubert, J.J., Kim, P.T. (1999). Empirical Bayesian estimation for combinations of multivariate bioassays. Biometrics 55(4):1035–1043. [Google Scholar]), which incorporated prior information on the model parameters based on Jeffreys’ rules. This investigation is carried out for the families of t-distribution and Cauchy-distribution based on the characteristics of bioassay theory since the t-distribution approaches the normal distribution which is the most commonly used distribution in the applications of bioassay as the degrees of freedom increases and the t-distribution approaches the Cauchy-distribution as the degrees of freedom approaches 1 which is also an important distribution in bioassay. A real data is used to illustrate the application of this investigation. This analysis further supports the application of the shrinkage Bayesian estimator to the theory of bioassay along with the empirical Bayesian estimator.     

Book Reviews

Book reviewed in this article:
The Design and Analysis of Longitudinal Studies: By Harvey Goldstein.
R. A. Fisher: An Appreciation. Edited by S. E. Fienberg & D. V. Hinkley.
Measurement in the Social Sciences. By R. A. Zeller & E. G. Carmines.
Analysis with Standard Contagious Diptriiutions. By J. B. Douglas.
Comparison of Box-Jenkins and Bonn Monetary Model Prediction Performance. By M. N. Bhattacharyya.
The Econometric Analysis of Time Series. By A. C. Harvey.
Estimation of Density from Line Transect Sampling of Biological Populations. By K. P. Bumham, D. R. Anderson & J. L. Laake.
Multrvariate Analysis-V. Proceedings of the Fifth International Symposium on Multivariate Analysis. Edited by P. R. Krishnaiah.
Recent Developments in Statistical Inference and Data Analysis. Edited by K. Matusita.
Contributions to Statistics. Edited by J. Jurecková.     

Book Reviews

Statistics: Learning in the Presence of Variation Robert L. Wardrop. Dubuque, IA: Wm. C. Brown Publishers, 1995. xxiv + 662 pp. $63.80. Reviewed by Allan J. Rossman

Modeling Experimental and Observational Data Clifford E. Lunneborg. Belmont, CA: Duxbury Press, 1994. xv + 506 pp. $40.95. Reviewed by Mark S. Handcock

Probability and Statistics for Engineers Richard Scheaffer and James T. McClave. Belmont, CA: Duxbury Press, 1995. xiv + 745 pp. $66.00. Reviewed by Gary D. Herrin     

Book Reviews

Books reviewed:
Bosq, D. Nonparametric Statistics for Stochastic Processes: Estimation and Prediction
Khuri, A.I., Mathew, T. & Sinha, B.K. Statistical Tests for Mixed Linear Models
Latouche, G. & Ramaswami, V. Introduction to Matrix Analytic Methods in Stochastic Modeling
Maitra, A.P. & Sudderth, W.D. Discrete Gambling and Stochastic Games
Rolski, T., Schmidli, H., Schmidt, V. & Teugels, J. Stochastic Processes for Insurance and Finance
Chung, K.L. & Williams, R.J. Introduction to Stochastic Integration     

Book Reviews

Books reviewed:
A.W. Kerr, H.K. Hall & S.A. Kozub, Doing Statistics with SPSS
P.I. Good, Resampling Methods: A Practical Guide to Data Analysis     

Reviews

Rachael Green Clemens reviews Usage and Usability Assessment: Library Practices and Concerns, James T. Deffenbaugh reviews Metadata Fundamentals for All Librarians, and Marguerite E. Horn reviews The Accidental Systems Librarian. Serials Review 2003; 29:325–329.     

On Comparing Cumulative Incidence Functions Using an Empirical Likelihood Ratio Type Test

We consider the competing risks problem with two risks and develop empirical likelihood ratio type tests for testing the null hypothesis that the cumulative incidence functions corresponding to these two risks are equal against the alternatives: (a) they are not equal and (b) they are linearly ordered. The asymptotic null distributions of the proposed test statistics are shown to have simple distribution-free representations in terms of a standard Brownian motion process. The results of a simulation study indicate that the proposed test for testing for the presence of the linear order is more powerful than a test designed for the same situation in Aly et al. (1994 Aly, E.A.A., Kochar, S.C., McKeague, I.W. (1994). Some tests for comparing cumulative incidence functions and cause-specific hazard rates. J. Am. Stat. Assoc. 89:994–999.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). To illustrate the theoretical results, we discuss an example involving survival times of mice exposed to radiation.     

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

The spectral measure plays a key role in the statistical modeling of multivariate extremes. Estimation of the spectral measure is a complex issue, given the need to obey a certain moment condition. We propose a Euclidean likelihood-based estimator for the spectral measure which is simple and explicitly defined, with its expression being free of Lagrange multipliers. Our estimator is shown to have the same limit distribution as the maximum empirical likelihood estimator of Einmahl and Segers (2009 Einmahl , J. H. J. , Segers , J. ( 2009 ). Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution . Ann. Statist. 37 ( 5B ): 2953 – 2989 .[Crossref], [Web of Science ®] , [Google Scholar]). Numerical experiments suggest an overall good performance and identical behavior to the maximum empirical likelihood estimator. We illustrate the method in an extreme temperature data analysis.     

Factor stochastic volatility with time varying loadings and Markov switching regimes

We generalize the factor stochastic volatility (FSV) model of Pitt and Shephard [1999. Time varying covariances: a factor stochastic volatility approach (with discussion). In: Bernardo, J.M., Berger, J.O., Dawid, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, vol. 6, Oxford University Press, London, pp. 547–570.] and Aguilar and West [2000. Bayesian dynamic factor models and variance matrix discounting for portfolio allocation. J. Business Econom. Statist. 18, 338–357.] in two important directions. First, we make the FSV model more flexible and able to capture more general time-varying variance–covariance structures by letting the matrix of factor loadings to be time dependent. Secondly, we entertain FSV models with jumps in the common factors volatilities through So, Lam and Li's [1998. A stochastic volatility model with Markov switching. J. Business Econom. Statist. 16, 244–253.] Markov switching stochastic volatility model. Novel Markov Chain Monte Carlo algorithms are derived for both classes of models. We apply our methodology to two illustrative situations: daily exchange rate returns [Aguilar, O., West, M., 2000. Bayesian dynamic factor models and variance matrix discounting for portfolio allocation. J. Business Econom. Statist. 18, 338–357.] and Latin American stock returns [Lopes, H.F., Migon, H.S., 2002. Comovements and contagion in emergent markets: stock indexes volatilities. In: Gatsonis, C., Kass, R.E., Carriquiry, A.L., Gelman, A., Verdinelli, I. Pauler, D., Higdon, D. (Eds.), Case Studies in Bayesian Statistics, vol. 6, pp. 287–302].     

Reviews of Books and Teaching Materials

Workshop Statistics: Discovery with Data and Minitab. Allan J. ROSSMAN and Beth L. CHANCE. New York: Springer-Verlag, 1998, xxviii + 486 pp. $39.95 (P).

Data Analysis with Microsoft Excel. Kenneth N. BERK and Patrick CAREY. Pacific Grove, CA: Duxbury, 1998, xxi + 503 pp. $29.95 (P), ISBN: 0-534-52929-1.

Statistical Laboratory Exercises Using Excel: A Guide to Understanding Data. Tania PRVAN and Peter PETOCZ. Brisbane: Jacaranda Wiley, 1998, vi + 57 pp., $15.95 (P + 1 disc), ISBN: 0-471-34050-2. Reviewed by Richard Cleary

Data, Statistics, and Decision Models with Excel. Donald L. HARNETT and James E. HORRELL. New York: Wiley, 1998, xviii + 605, $93.95, ISBN: 0-471-13398-1. Reviewed by Sue B. Schou

Maple V© Student Version: Release 5. Waterloo Maple, Inc., New York: Springer-Verlag, 1998, $99.

Statistics with Stata 5. Lawrence C. HAMILTON. Pacific Grove, CA: Duxbury, 1998, x + 325 pp. $34.95, ISBN: 0-534-26559-6. Reviewed by Richard Goldstein     

BOOK REVIEWS

Book reviewed in this article:
Mendel, G. Experiments in Plant Hybridisation.
David, F. N., Kendall, M. G., and Barton, D. E. Symmetric Function and Allied Tables.
Van Berckel, J. A. Th. M., Brandt Corstius, H., Mokken, R. J., and Van Wijngaarden, A. Formal Properties of Newspaper Dutch.
Thorp, E. O. Elementary Probability.
Mode, E. B. Elements of Probability and Statistics.
Lee, A. M. Applied Queuing Theory.
Meyer, P. A. Probability and Potentials.
Eilon, S., Hall, R. I., and King, J. R. Exercises in Industrial Management.     

Book Reviews

Book reviewed in this article:
Some Basic Theory for Statistical Inference. By E. J. G. Pitman
An Introduction to Multivariate Statistics. By M. S. Srivastava & C. G. Khatri
Forecasting. Proceedings of the Institute of Statisticians Annual Conference, Cambridge, 1976. Edited by O. D. Anderson
Statistical Method in Biological Assay. (3rd edition). By D. J. Finney
Statistical Epidemiology in Veterinary Science. By F. B. Leech & K. C. Sellars
Branching Processes with Continuous State Space. By P. J. M. Kallen-berg
An Introduction to Probability and Statistics Using BASIC. By R. A. Groeneveld     

Book Reviews

Book reviewed in this article:
De Haan, L. On Regular Variation, and Its Applications to the Weak Convergence of Sample Extremes. Mathematical Centre Tracts No. 32
Johnson, N. L., and Kotz, S. Distributions in Statistics: Continuous Univariate Distributions.
Joiner, Brian L., Laubscher, N. F., Brown, Eleanor S., and Levy, Bert. An Author and Permuted Title Index to Selected Statistical Journals.
Kendall, M. G. Bank Correlation Methods. 4th edition
Lass, H., and Gottlieb, P. Probability and Statistics.
Neveu, J. Bases Mathematiques du Caleul des Probabilites.
Maddox, I. J. Elements of Functional Analysis.
Aitchison, John. Choice Against Chance: An Introduction to Statistical Decision Theory.
Chat field, C. Statistics for Technology.
Cramer, H. Random Variables and Probability Distributions. Third edition
Feller, William (1966). An Introduction to Probability Theory and Its Application.
De Leve, G., Tijms, H. C, and Weeda, P. J. Generalized MarTcovian Decision Processes (Applications).
Hajek, J. Nonparametric Statistics.
Hinderer, K. Foundations of Non-stationary Dynamic Programming with Discrete Time Parameter.
Kreyszig, E. Introductory Mathematical Statistics. Principles and Methods.
Patil, G. P. (Editor). Random Counts in Models and Structures.
Pearson, E. S., and Kendall, M. G. (Editors). Studies in the History of Statistics and Probability.
Puri, M. L. (Editor). Nonparametric Techniques in Statistical Inference.
Yates, Frank. Experimental Design—Selected Papers.     

BOOK REVIEWS

Book reviewed in this article:
Neyman, J., and Pearson, E. S. Jiont Statistical Papers of J. Neyman and E. S. Pearson.
Pearson, E.S. The Selected Papers of E. S. Pearson.
Doornbos, R. Slippage Tests.
Fabrycky, W. J., and Torgersen, P. E. Operations Economy: Industrial Applications of Operations Research.
Krishnaiah, P. R. (Ed.). Multivariate analysis.
Takacs, Lajos. combinatorial Mehods in the Theory of stcohastic processes.
Yovits, Gilford, Wilcox, Staveley, Lerner (Editors). Research Program Effectiveness.
Noether, G. E. Elements of Nonparametric Statistics.
Hodges, J. L., and Lehmann, E. L. elements of Finite Probability.
Lamb, H. H. The Changing Climate.
Runyon, R. P., and Haber, A. Fundamentals of Behavioral Satatistics.