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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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