Showing papers in "Journal of The Royal Statistical Society Series A-statistics in Society in 1988"
5,228 citations
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.
3,216 citations
1,306 citations
TL;DR: Lehmann as discussed by the authors, Testing Statistical Hypotheses (2nd ed.). By E. L. Lehmann, 1986. xx, 600p. £44.13.
Abstract: 13. Testing Statistical Hypotheses (2nd ed.). By E. L. Lehmann. ISBN 0–471–84083–1, Wiley, 1986. xx, 600p. £44.15.
1,004 citations
TL;DR: In this paper, the authors review the available research, discuss alternative suggestions for conducting unbiased meta-analysis and suggest some scientific policy measures which could improve the quality of published data in the long term.
Abstract: Publication bias, the phenomenon in which studies with positive results are more likely to be published than studies with negative results, is a serious problem in the interpretation of scientific research. Various hypothetical models have been studied which clarify the potential for bias and highlight characteristics which make a study especially susceptible to bias. Empirical investigations have supported the hypothesis that bias exists and have provided a quantitative assessment of the magnitude of the problem. The use of meta‐analysis as a research tool has focused attention on the issue, since naive methodologies in this area are especially susceptible to bias. In this paper we review the available research, discuss alternative suggestions for conducting unbiased meta‐analysis and suggest some scientific policy measures which could improve the quality of published data in the long term.
744 citations
TL;DR: The Statistical Tools for Simulation Practitioners (SSPP) as discussed by the authors is a set of tools for simulation practitioners that can be found in the Textbooks and Monographs Series, Vol. 76.
Abstract: 17. Statistical Tools for Simulation Practitioners (Statistics: Textbooks and Monographs Series, Vol. 76). By J. P. C. Kleijnen. ISBN 0 8247 73333 0. Dekker, New York, 1987. 448 pp. $83.50.
607 citations
TL;DR: In this paper, one-way analysis of variance with fixed effects was used to test whether the data fit the one-Way ANOVA model. But the results showed that the model was not robust enough to handle large numbers of samples.
Abstract: Preface.1. Data Screening.1.1 Variables and Their Classification.1.2 Describing the Data.1.2.1 Errors in the Data.1.2.2 Descriptive Statistics.1.2.3 Graphical Summarization.1.3 Departures from Assumptions.1.3.1 The Normal Distribution.1.3.2 The Normality Assumption.1.3.3 Transformations.1.3.4 Independence.1.4 Summary.Problems.References.2. One-Way Analysis of Variance Design.2.1 One-Way Analysis of Variance with Fixed Effects.2.1.1 Example.2.1.2 The One-Way Analysis of Variance Model with Fixed Effects.2.1.3 Null Hypothesis: Test for Equality of Population Means.2.1.4 Estimation of Model Terms.2.1.5 Breakdown of the Basic Sum of Squares.2.1.6 Analysis of Variance Table.2.1.7 The F Test.2.1.8 Analysis of Variance with Unequal Sample Sizes.2.2 One-Way Analysis of Variance with Random Effects.2.2.1 Data Example.2..2.2 The One-Way Analysis of Variance Model with Random Effects.2.2.3 Null Hypothesis: Test for Zero Variance of Population Means.2.2.4 Estimation of Model Terms.2.2.5 The F Test.2.3 Designing an Observational Study or Experiment.2.3.1 Randomization for Experimental Studies.2.3.2 Sample Size and Power.2.4 Checking if the Data Fit the One-Way ANOVA Model.2.4.1 Normality.2.4.2 Equality of Population Variances.2.4.3 Independence.2.4.4 Robustness.2.4.5 Missing Data.2.5 What to Do if the Data Do Not Fit the Model.2.5.1 Making Transformations.2.5.2 Using Nonparametric Methods.2.5.3 Using Alternative ANOVAs.2.6 Presentation and Interpretation of Results.2.7 Summary.Problems.References.3. Estimation and Simultaneous Inference.3.1 Estimation for Single Population Means.3.1.1 Parameter Estimation.3.1.2 Confidence Intervals.3.2 Estimation for Linear Combinations of Population Means.3.2.1 Differences of Two Population Means.3.2.2 General Contrasts for Two or More Means.3.2.3 General Contrasts for Trends.3.3 Simultaneous Statistical Inference.3.1.1 Straightforward Approach to Inference.3.3.2 Motivation for Multiple Comparison Procedures and Terminology.3.3.3 The Bonferroni Multiple Comparison Method.3.3.4 The Tukey Multiple Comparison Method.3.3.5 The Scheffe Multiple Comparison Method.3.4 Inference for Variance Components.3.5 Presentation and Interpretation of Results.3.6 Summary.Problems.References.4. Hierarchical or Nested Design.4.1 Example.4.2 The Model.4.3 Analysis of Variance Table and F Tests.4.3.1 Analysis of Variance Table.4.3.2 F Tests.4.3.3 Pooling.4.4 Estimation of Parameters.4.4.1 Comparison with the One-Way ANOVA Model of Chapter 2.4.5 Inferences with Unequal Sample Sizes.4.5.1 Hypothesis Testing.4.5.2 Estimation.4.6 Checking If the Data Fit the Model.4.7 What to Do If the Data Don't Fit the Model.4.8 Designing a Study.4.8.1 Relative Efficiency.4.9 Summary.Problems.References.5. Two Crossed Factors: Fixed Effects and Equal Sample Sizes.5.1 Example.5.2 The Model.5.3 Interpretation of Models and Interaction.5.4 Analysis of Variance and F Tests.5.5 Estimates of Parameters and Confidence Intervals.5.6 Designing a Study.5.7 Presentation and Interpretation of Results.5.8 Summary.Problems.References.6 Randomized Complete Block Design.6.1 Example.6.2 The Randomized Complete Block Design.6.3 The Model.6.4 Analysis of Variance Table and F Tests.6.5 Estimation of Parameters and Confidence Intervals.6.6 Checking If the Data Fit the Model.6.7 What to Do if the Data Don't Fit the Model.6.7.1 Friedman's Rank Sum Test.6.7.2 Missing Data.6.8 Designing a Randomized Complete Block Study.6.8.1 Experimental Studies.6.8.2 Observational Studies.6.9 Model Extensions.6.10 Summary.Problems.References.7. Two Crossed Factors: Fixed Effects and Unequal Sample Sizes.7.1 Example.7.2 The Model.7.3 Analysis of Variance and F Tests.7.4 Estimation of Parameters and Confidence Intervals.7.4.1 Means and Adjusted Means.7.4.2 Standard Errors and Confidence Intervals.7.5 Checking If the Data Fit the Two-Way Model.7.6 What To Do If the Data Don't Fit the Model.7.7 Summary.Problems.References.8. Crossed Factors: Mixed Models.8.1 Example.8.2 The Mixed Model.8.3 Estimation of Fixed Effects.8.4 Analysis of Variance.8.5 Estimation of Variance Components.8.6 Hypothesis Testing.8.7 Confidence Intervals for Means and Variance Components.8.7.1 Confidence Intervals for Population Means.8.7.2 Confidence Intervals for Variance Components.8.8 Comments on Available Software.8.9 Extensions of the Mixed Model.8.9.1 Unequal Sample Sizes.8.9.2 Fixed, Random, or Mixed Effects.8.9.3 Crossed versus Nested Factors.8.9.4 Dependence of Random Effects.8.10 Summary.Problems.References.9. Repeated Measures Designs.9.1 Repeated Measures for a Single Population.9.1.1 Example.9.1.2 The Model.9.1.3 Hypothesis Testing: No Time Effect.9.1.4 Simultaneous Inference.9.1.5 Orthogonal Contrasts.9.1.6 F Tests for Trends over Time.9.2 Repeated Measures with Several Populations.9.2.1 Example.9.2.2 Model.9.2.3 Analysis of Variance Table and F Tests.9.3 Checking if the Data Fit the Repeated Measures Model.9.4 What to Do if the Data Don't Fit the Model.9.5 General Comments on Repeated Measures Analyses.9.6 Summary.Problems.References.10. Linear Regression: Fixed X Model.10.1 Example.10.2 Fitting a Straight Line.10.3 The Fixed X Model.10.4 Estimation of Model Parameters and Standard Errors.10.4.1 Point Estimates.10.4.2 Estimates of Standard Errors.10.5 Inferences for Model Parameters: Confidence Intervals.10.6 Inference for Model Parameters: Hypothesis Testing.10.6.1 t Tests for Intercept and Slope.10.6.2 Division of the Basic Sum of Squares.10.6.3 Analysis of Variance Table and F Test.10.7 Checking if the Data Fit the Regression Model.10.7.1 Outliers.10.7.2 Checking for Linearity.10.7.3 Checking for Equality of Variances.10.7.4 Checking for Normality.10.7.5 Summary of Screening Procedures.10.8 What to Do if the Data Don't Fit the Model.10.9 Practical Issues in Designing a Regression Study.10.9.1 Is Fixed X Regression an Appropriate Technique?10.9.2 What Values of X Should Be Selected?10.9.3 Sample Size Calculations.10.10 Comparison with One-Way ANOVA.10.11 Summary.Problems.References.11. Linear Regression: Random X Model and Correlation.11.1 Example.11.1.1 Sampling and Summary Statistics.11.2 Summarizing the Relationship Between X and Y.11.3 Inferences for the Regression of Y and X.11.3.1 Comparison of Fixed X and Random X Sampling.11.4 The Bivariate Normal Model.11.4.1 The Bivariate Normal Distribution.11.4.2 The Correlation Coefficient.11.4.3 The Correlation Coefficient: Confidence Intervals and Tests.11.5 Checking if the Data Fit the Random X Regression Model.11.5.1 Checking for High-Leverage, Outlying, and Influential Observations.11.6 What to Do if the Data Don't Fit the Random X Model.11.6.1 Nonparametric Alternatives to Simple Linear Regression.11.6.2 Nonparametric Alternatives to the Pearson Correlation.11.7 Summary.Problem.References.12. Multiple Regression.12.1 Example.12.2 The Sample Regression Plane.12.3 The Multiple Regression Model.12.4 Parameters Standard Errors, and Confidence Intervals.12.4.1 Prediction of E(Y\\X1,...,Xk).12.4.2 Standardized Regression Coefficients.12.5 Hypothesis Testing.12.5.1 Test That All Partial Regression Coefficients Are 0.12.5.2 Tests that One Partial Regression Coefficient is 0.12.6 Checking If the Data Fit the Multiple Regression Model.12.6.1 Checking for Outlying, High Leverage and Influential Points.12.6.2 Checking for Linearity.12.6.3 Checking for Equality of Variances.12.6.4 Checking for Normality of Errors.12.6.5 Other Potential Problems.12.7 What to Do If the Data Don't Fit the Model.12.8 Summary.Problems.References.13. Multiple and Partial Correlation.13.1 Example.13.2 The Sample Multiple Correlation Coefficient.13.3 The Sample Partial Correlation Coefficient.13.4 The Joint Distribution Model.13.4.1 The Population Multiple Correlation Coefficient.13.4.2 The Population Partial Correlation Coefficient.13.5 Inferences for the Multiple Correlation Coefficient.13.6 Inferences for Partial Correlation Coefficients.13.6.1 Confidence Intervals for Partial Correlation Coefficients.13.6.2 Hypothesis Tests for Partial Correlation Coefficients.13.7 Checking If the Data Fit the Joint Normal Model.13.8 What to Do If the Data Don't Fit the Model.13.9 Summary.Problems.References.14. Miscellaneous Topics in Regression.14.1 Models with Dummy Variables.14.2 Models with Interaction Terms.14.3 Models with Polynomial Terms.14.3.1 Polynomial Model.14.4 Variable Selection.14.4.1 Criteria for Evaluating and Comparing Models.14.4.2 Methods for Variable Selection.14.4.3 General Comments on Variable Selection.14.5 Summary.Problems.References.15. Analysis of Covariance.15.1 Example.15.2 The ANCOVA Model.15.3 Estimation of Model Parameters.15.4 Hypothesis Tests.15.5 Adjusted Means.15.5.1 Estimation of Adjusted Means and Standard Errors.15.5.2 Confidence Intervals for Adjusted Means.15.6 Checking If the Data Fit the ANCOVA Model.15.7 What to Do if the Data Don't Fit the Model.15.8 ANCOVA in Observational Studies.15.9 What Makes a Good Covariate.15.10 Measurement Error.15.11 ANCOVA versus Other Methods of Adjustment.15.12 Comments on Statistical Software.15.13 Summary.Problems.References.16. Summaries, Extensions, and Communication.16.1 Summaries and Extensions of Models.16.2 Communication of Statistics in the Context of Research Project.References.Appendix A.A.1 Expected Values and Parameters.A.2 Linear Combinations of Variables and Their Parameters.A.3 Balanced One-Way ANOVA, Expected Mean Squares.A.3.1 To Show EMS(MSa) = sigma2 + n SIGMAai= 1 alpha2i /(a - 1).A.3.2 To Show EMS(MSr) = sigma2.A.4 Balanced One-Way ANOVA, Random Effects.A.5 Balanced Nested Model.A.6 Mixed Model.A.6.1 Variances and Covariances of Yijk.A.6.2 Variance of Yi.A.6.3 Variance of Yi. - Yi'..A.7 Simple Linear Regression-Derivation of Least Squares Estimators.A.8 Derivation of Variance Estimates from Simple Linear Regression.Appendix B.Index.
360 citations
TL;DR: This volume has its origin in the Fifth, Sixth and Seventh Workshops on "Maximum-Entropy and Bayesian Methods in Applied Statistics," held at the University of Wyoming and at Seattle University, August 5-8, 1986, and August 4-7, 1987.
Abstract: This volume has its origin in the Fifth, Sixth and Seventh Workshops on "Maximum-Entropy and Bayesian Methods in Applied Statistics," held at the University of Wyoming, August 5-8, 1985, and at Seattle University, August 5-8, 1986, and August 4-7, 1987. It was anticipated that the proceedings of these workshops would be combined, so most of the papers were not collected until after the seventh workshop. Because most of the papers in this volume are in the nature of advancing theory or solving specific problems, as opposed to status reports, it is believed that the contents of this volume will be of lasting interest to the Bayesian community. The workshop was organized to bring together researchers from different fields to critically examine maximum-entropy and Bayesian methods in science and engineering as well as other disciplines. Some of the papers were chosen specifically to kindle interest in new areas that may offer new tools or insight to the reader or to stimulate work on pressing problems that appear to be ideally suited to the maximum-entropy or Bayesian method. These workshops and their proceedings could not have been brought to their final form without the support or help of a number of people.
320 citations
TL;DR: Methode d'ajustement de courbes centiles lissees a des donnees de reference, basee sur la famille de transformation de Box et Cox as discussed by the authors.
Abstract: Methode d'ajustement de courbes centiles lissees a des donnees de reference, basee sur la famille de transformation de Box et Cox
294 citations
249 citations
TL;DR: Little and Rubin this article presented Statistical Analysis with Missing Data, a method for statistical analysis with missing data, which can be used to identify missing data points in missing data sets, but it requires a large amount of data.
Abstract: 19. Statistical Analysis with Missing Data. By R. J. A. Little and D. B. Rubin. ISBN 0 471 80254 9. Wiley, Chichester, 1987. xiv + 278 pp. £32.05.
TL;DR: In this article, Kendall's Advanced Theory of Statistics (AToS), 5th Edition, 5th edition, is discussed. And the authors propose a new distribution theory for distribution theory.
Abstract: 24. Kendall's Advanced Theory of Statistics. Volume 1. Distribution Theory, 5th Edition. By Alan Stuart and J. Keith Ord. ISBN 0 85264 285 7. Griffin, 1987. xv, 604 p. £40.00.
TL;DR: 17. Modern Statistical Methods in Chronic Disease Epidemiology: Foundations of Statistical Methodology, 2nd Ed.
Abstract: 17. Modern Statistical Methods in Chronic Disease Epidemiology. Edited by S. H. Moolgavkar and R. L. Prentice. ISBN 0 471 839043. Wiley, 1986, 282p. Unpriced. (Proceedings of a Conference sponsored by SIAM Institute for Mathematics and Society).
TL;DR: The first chapter is by Diaconis and serves as an introductory and motivating piece as discussed by the authors, where he gives seven remedies for the all too common "magical thinking" (his code words for irrational thinking).
Abstract: Edited by three well-known and respected statisticians, this book is another on exploratory data analysis (EDA), and is part of the prestigious Wiley Series on Probability and Mathematical Statistics. The contributors, in addition to the three editors, seem to be highly competent and write in a way that conveys a healthy exuberance for the work they are doing. The first chapter is by Diaconis and serves as an introductory and motivating piece. This chapter talks about problems with drawing correct conclusions from data, and it relies heavily on the work summarized in Nisbett and Ross (1980). Anyone interested in the psychological research on decisionmaking should refer to that book, although without a glossary of the jargon, it is difficult to read. The impression created is that this first chapter will build toward an advocacy of EDA and this is largely correct. Diaconis gives us seven remedies for the all too common "magical thinking" (his code words for irrational thinking). Five of them are complex mixtures of general methodological or statistical techniques such as cross-validation and bootstrapping. One remedy is negative in that it advocates not reporting p-values, apparently giving up on them being interpreted correctly. The seventh remedy is titled "remedies to come" and this is where EDA is classified. The focus of EDA is clarified when Diaconis says "Yet, none of the classical theories of statistics comes close to capturing what a real scientist does when exploring new data in a real scientific problem" (p. 22). The key is the focus on analysis of new data, but the implication is that the problem area is also new. In other words, EDA appears to be most useful when there is no theory (probabilistic or otherwise). More will be said about this later. In summary, this chapter, unlike many of the others, is a little unfocused, but intellectually very interesting and stimulating. It deals more with ideas than with algorithms.
TL;DR: It is shown here how the model derived recently in [Bouchut-Boyaval, M3AS2] can be modified to suit the needs of a large number of users and also helps clarify the role of uncertainty in the model.
Abstract: 10. Multivariate Statistical Simulation. By Mark E. Johnson. ISBN 0 471 82290 6. Wiley, 1987. 230p. £33.75.
TL;DR: In this article, the authors present tools and solutions to the following problems: 1. Problems, Tools, Tools and Tools, 2. Premiums, 4. Reinsurance, 5. Retentions, 6. Statistics, 7. Reserves, 8.
Abstract: 1. Problems.- 2. Tools.- 3. Premiums.- 4. Reinsurance.- 5. Retentions.- 6. Statistics.- 7. Reserves.- 8. Solutions.- References.
TL;DR: In this article, N. N. Ethier and T. G. Kurtz present a Markov Processes, Characterization and Convergence (MPCC) for Markovian Markov processes.
Abstract: 9. Markov Processes, Characterization and Convergence. By S. N. Ethier and T. G. Kurtz. ISBN 0 471 08186 8. Wiley, Chichester, 1986. 534 pp. £49.10.
TL;DR: An introduction to medical Statistics: An Introduction to Medical Statistics by M. Bland.
Abstract: 4. An Introduction to Medical Statistics. By M. Bland. ISBN 0 19 261502 5. Oxford Medical Publications, Oxford, 1987. 366 pp. £9.95.
TL;DR: In this paper, the authors describe the data available on the UK AIDS epidemic and see to what extent extrapolation forecasts can be reliably made, and the purpose of this paper is to describe the available data.
Abstract: For both scientific and administrative reasons it is of vital importance to forecast the future course of the AIDS epidemic. There are essentially two ways of doing this. On the one hand, a mathematical model of the spread of the disease can be constructed and used to provide forecasts for as far ahead as is desired. At the other end of the spectrum, numbers of recorded cases can be plotted against time and the resulting curve extrapolated forwards. The model-based forecasts rely upon estimates of the model parameters which must to some extent be supplied by reference to the case data; as will appear, the extrapolation forecasts are far from being model-free. The purpose of this paper is to describe the data available on the UK epidemic and to see to what extent extrapolation forecasts can be reliably made.