Cargando…
Smoothing Spline ANOVA Models
Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data anal...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
|
| Edición: | 2nd ed. 2013. |
| Colección: | Springer Series in Statistics,
297 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-1-4614-5369-7 | ||
| 003 | DE-He213 | ||
| 005 | 20220119081229.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 130125s2013 xxu| s |||| 0|eng d | ||
| 020 | |a 9781461453697 |9 978-1-4614-5369-7 | ||
| 024 | 7 | |a 10.1007/978-1-4614-5369-7 |2 doi | |
| 050 | 4 | |a QA276-280 | |
| 072 | 7 | |a PBT |2 bicssc | |
| 072 | 7 | |a MAT029000 |2 bisacsh | |
| 072 | 7 | |a PBT |2 thema | |
| 082 | 0 | 4 | |a 519.5 |2 23 |
| 100 | 1 | |a Gu, Chong. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Smoothing Spline ANOVA Models |h [electronic resource] / |c by Chong Gu. |
| 250 | |a 2nd ed. 2013. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2013. | |
| 300 | |a XVIII, 433 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Springer Series in Statistics, |x 2197-568X ; |v 297 | |
| 505 | 0 | |a Introduction -- Model Construction -- Regression with Gaussian-Type Responses -- More Splines -- Regression and Exponential Families -- Regression with Correlated Responses -- Probability Density Estimation -- Hazard Rate Estimation -- Asymptotic Convergence -- Penalized Pseudo Likelihood. | |
| 520 | |a Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. | ||
| 650 | 0 | |a Statistics . | |
| 650 | 1 | 4 | |a Statistical Theory and Methods. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781461453703 |
| 776 | 0 | 8 | |i Printed edition: |z 9781489989840 |
| 776 | 0 | 8 | |i Printed edition: |z 9781461453680 |
| 830 | 0 | |a Springer Series in Statistics, |x 2197-568X ; |v 297 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4614-5369-7 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||