Titlebook: Smoothing Spline ANOVA Models; Chong Gu Book 2013Latest edition Springer Science+Business Media New York 2013 ANOVA.ANOVA models.Spline sm

碌碌之人

Prostatism

Regression with Responses from Exponential Families,For responses from exponential family distributions, (1.4) of Example 1.1 defines penalized likelihood regression. Among topics of primary interest are the selection of smoothing parameters, the computation of the estimates, the asymptotic behavior of the estimates, and various data analytical tools.

使?jié)M足

Hazard Rate Estimation,For right-censored lifetime data with possible left-truncation, (1.6) of Example 1.3 defines penalized likelihood hazard estimation. Of interest are the selection of smoothing parameters, the computation of the estimates, and the asymptotic behavior of the estimates.

消耗

Asymptotic Convergence,In this chapter, we develop an asymptotic theory concerning the rates of convergence of penalized likelihood estimates to the target functions as the sample size goes to infinity. The rates are calculated in terms of problem-specific loss functions derived from the respective stochastic settings.

stratum-corneum

Penalized Pseudo Likelihood,The density estimation of (7.301) is infeasible on high-dimensional . due to the prohibitive cost of . via multivariate numerical integration.

arthroscopy

pus840

Model Construction,. of (1.3). The deterministic part is specified via .(.), which defines the notion of smoothness for functions on domain .. The stochastic part is characterized by .(.), which reflects the sampling structure of the data.

植物茂盛

More Splines,ndard choices on continuous domains, but generalizations or restrictions are sometimes called for by the nature of the applications. The technical underpinnings of the variants are generally different from that of polynomial splines, but once the reproducing kernels are specified, everything else remains largely intact.

情節(jié)劇

Regression with Correlated Responses,dentifiable from each other, the correlation can not be arbitrary but structured around a limited number of parameters, say γ, and the correlation structure should not be dependent on the covariate .. Of primary interest is the selection of tuning parameters, which now consist of the smoothing parameters in λ.(η) and the correlation parameters γ.

粗糙

Probability Density Estimation,he computation of the estimates, and the asymptotic behavior of the estimates. Variants of (1.5) are also called for to accommodate samples subject to selection bias and samples from conditional distributions.