Using Model/Data Simulations to Detect
Download:
|
|
by Jim Albert, Patricia Williamson
http://www-math.bgsu.edu/~albert/papers/streak.ps
Add To MetaCart
Abstract:
A simulation-based approach is proposed for approximating a Bayesian analysis. Parameters and data are simulated from a Bayesian model and inference about a parameter is performed by exploring the set of simulated parameter values conditional on a set of values of a simulated statistic. The approach is used to learn about parameters of a streaky model on the basis of a statistic used to measure streakiness. The method is illustrated to detect streakiness in baseball hitting data and basketball shooting data. 1
Citations
| 520 | Generalized Linear Models – McCullagh, Nelder - 1989 |
| 19 | The hot hand in basketball: On the misperception of random sequences – Gilovich, Vallone, et al. - 1985 |
| 11 | A statistical analysis of hitting streaks in baseball (Disc: p1184--1196 – Albright - 1993 |
| 4 | The cold facts about the “hot hand” in basketball – Tversky, Gilovich - 1989 |
| 2 | Teaching Bayes' rule: a data-oriented approach – Albert - 1997 |
| 2 | Choice models for predicting division winners in major league baseball – Berry, Hartigan - 1993 |
| 2 | It's okay to believe in the `hot hand – Larkey, Smith, et al. - 1989 |
| 1 | Comment – Albert - 1993 |
| 1 | The home run hitting of Mike Schmidt – Albert - 1998 |
| 1 | Judging who's hot and who's not – Stern - 1997 |
