Bootstrapping a nonparametric approach to statistical inference Christopher Z. Mooney, Robert D. Duval.
Series Sage university papers series. Quantitative applications in the social sciences ; no. 07-095Detalles de publicación: Newbury Park, Calif. Sage Publications c1993.Descripción: vi, 73 p. ill. 22 cmISBN:- 080395381X
- 300/.1/5195 20
- HA31.2.M66 1993
| Imagen de cubierta | Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Ubicación en estantería | Signatura topográfica | Materiales especificados | Info Vol | URL | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | Prioridad de la cola de reserva de ejemplar | Reservas para cursos | |
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| Libro | Biblioteca Encarnación Valdés Colección General bev | 300.15195 M818b (Navegar estantería(Abre debajo)) | Disponible | 80000001717001 |
Descripciones mejoradas de Syndetics:
This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . . --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
Includes bibliographical references (p. 68-72).
Tabla de contenidos provista por Syndetics
- Part 1 Introduction
- Traditional Parametric Statistical Inference
- Bootstrap Statistical Inference
- Bootstrapping a Regression Model
- Theoretical Justification
- The Jackknife
- Monte Carlo Evaluation of the Bootstrap
- Part 2 Statistical Inference Using the Bootstrap
- Bias Estimation
- Bootstrap Confidence Intervals
- Part 3 Applications of Bootstrap Confidence Intervals
- Confidence Intervals for Statistics With Unknown Sampling Distributions
- Inference When Traditional Distributional Assumptions Are Violated
- Part 4 Conclusion
- Future Work
- Limitations of the Bootstrap
- Concluding Remarks
Notas de autor provistas por Syndetics
Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs.Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy.Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom