<span><p>This book providesย an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students.ย It uniquely combines a hands-on approach to data analysis โ supported by numerous real data examples and reusable [R] code โ with a rigorous treat
Probability Models and Statistical Analyses for Ranking Data
โ Scribed by Douglas E. Critchlow, Michael A. Fligner (auth.), Michael A. Fligner, Joseph S. Verducci (eds.)
- Publisher
- Springer-Verlag New York
- Year
- 1993
- Tongue
- English
- Leaves
- 329
- Series
- Lecture Notes in Statistics 80
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
In June of 1990, a conference was held on Probablity Models and Statistiยญ cal Analyses for Ranking Data, under the joint auspices of the American Mathematical Society, the Institute for Mathematical Statistics, and the Society of Industrial and Applied Mathematicians. The conference took place at the University of Massachusetts, Amherst, and was attended by 36 participants, including statisticians, mathematicians, psychologists and sociologists from the United States, Canada, Israel, Italy, and The Netherยญ lands. There were 18 presentations on a wide variety of topics involving ranking data. This volume is a collection of 14 of these presentations, as well as 5 miscellaneous papers that were contributed by conference participants. We would like to thank Carole Kohanski, summer program coordinator for the American Mathematical Society, for her assistance in arranging the conference; M. Steigerwald for preparing the manuscripts for publication; Martin Gilchrist at Springer-Verlag for editorial advice; and Persi Diaconis for contributing the Foreword. Special thanks go to the anonymous referees for their careful readings and constructive comments. Finally, we thank the National Science Foundation for their sponsorship of the AMS-IMS-SIAM Joint Summer Programs. Contents Preface vii Conference Participants xiii Foreword xvii 1 Ranking Models with Item Covariates 1 D. E. Critchlow and M. A. Fligner 1. 1 Introduction. . . . . . . . . . . . . . . 1 1. 2 Basic Ranking Models and Their Parameters 2 1. 3 Ranking Models with Covariates 8 1. 4 Estimation 9 1. 5 Example. 11 1. 6 Discussion. 14 1. 7 Appendix . 15 1. 8 References.
โฆ Table of Contents
Front Matter....Pages i-xxiii
Ranking Models with Item Covariates....Pages 1-19
Nonparametric Methods of Ranking from Paired Comparisons....Pages 20-36
On the Babington Smith Class of Models for Rankings....Pages 37-52
Latent Structure Models for Ranking Data....Pages 53-74
Modelling and Analysing Paired Ranking Data....Pages 75-91
Maximum Likelihood Estimation in Mallowsโs Model Using Partially Ranked Data....Pages 92-107
Extensions of Mallowsโ ฯ Model....Pages 108-139
Rank Correlations and the Analysis of Rank-Based Experimental Designs....Pages 140-156
Applications of Thurstonian Models to Ranking Data....Pages 157-172
Probability Models on Rankings and the Electoral Process....Pages 173-195
Permutations and Regression Models....Pages 196-215
Aggregation Theorems and the Combination of Probabilistic Rank Orders....Pages 216-240
A Nonparametric Distance Model for Unidimensional Unfolding....Pages 241-276
Front Matter....Pages 277-277
Models on Spheres and Models for Permutations....Pages 278-283
Complete Consensus and Order Independence: Relating Ranking and Choice....Pages 284-288
Ranking From Paired Comparisons by Minimizing Inconsistency....Pages 289-293
Graphical Techniques for Ranked Data....Pages 294-298
Matched Pairs and Ranked Data....Pages 299-306
โฆ Subjects
Probability Theory and Stochastic Processes
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