Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications - Yang Aijun
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
In the practice of statistical modeling, it is often desirable to have an accurate predictive model. Modern data sets usually have a large number of predictors.Hence parsimony is especially an important issue. Best-subset selection is a conventional method of variable selection. Due to the large number of variables with relatively small sample size and severe collinearity among the variables, standard stati ... Täydellinen kuvaus
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Kuvaus
In the practice of statistical modeling, it is often desirable to have an accurate predictive model. Modern data sets usually have a large number of predictors.Hence parsimony is especially an important issue. Best-subset selection is a conventional method of variable selection. Due to the large number of variables with relatively small sample size and severe collinearity among the variables, standard statistical methods for selecting relevant variables often face difficulties. Bayesian stochastic search variable selection has gained much empirical success in a variety of applications. This book, therefore, proposes a modified Bayesian stochastic variable selection approach for variable selection and two/multi-class classification based on a (multinomial) probit regression model.We demonstrate the performance of the approach via many real data. The results show that our approach selects smaller numbers of relevant variables and obtains competitive classification accuracy based on obtained results.
Lisätietoja
| Kirjoittaja | Yang Aijun |
|---|---|
| Julkaisija | LAP LAMBERT Academic Publishing |
| Julkaisuvuosi | 2011 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783846505717 |