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二阶导数函数型回归及其近红外光谱应用AI 解读

Summary A linear regression method to predict a scalar from a discretized smooth function is presented. The method takes into account the functional nature of the predictors and the importance of the second derivative in spectroscopic applications. This motivates a functional inner product that can be used as a roughness penalty. Using this inner product, we derive a linear prediction method that is similar to ridge regression but with different shrinkage characteristics. We…

基于核方法的混合密度非参数估计AI 解读

Editor's note: This manuscript was first submitted in May 1996, and a first review with a request for revision was completed in September 1996. Costas Goutis lost his life in a scuba diving accident in July 1996. The associate editor, Jianqing Fan, graciously offered to carry out the required revisions. This article presents a method for estimating the latent distribution of a mixture model. The method is motivated by the standard kernel density estimation, but instead of…

利用核方法非参数估计混合密度AI 解读

Editor's note: This manuscript was first submitted in May 1996, and a first review with a request for revision was completed in September 1996. Costas Goutis lost his life in a scuba diving accident in July 1996. The associate editor, Jianqing Fan, graciously offered to carry out the required revisions. This article presents a method for estimating the latent distribution of a mixture model. The method is motivated by the standard kernel density estimation, but instead of…

基于平滑因子的偏最小二乘回归AI 解读

In this article we present a modification of partial least squares regression to account for inherent nonexchangeabilities of the columns of the design matrix. In chemometrics applications it is common to write the matrix as a bilinear form of latent variables and loadings. These loadings are often interpreted as sampled values of functions; hence they should exhibit a degree of smoothness. Our method forces the partial least squares factors to be smooth, by using a…

基于平滑因子的偏最小二乘回归AI 解读

In this article we present a modification of partial least squares regression to account for inherent nonexchangeabilities of the columns of the design matrix. In chemometrics applications it is common to write the matrix as a bilinear form of latent variables and loadings. These loadings are often interpreted as sampled values of functions; hence they should exhibit a degree of smoothness. Our method forces the partial least squares factors to be smooth, by using a…

偏最小二乘算法产生收缩估计量AI 解读

We give a geometric proof that the estimates of a regression model derived by using partial least squares shrink the ordinary least squares estimates. The proof is based on a sequential construction algorithm of partial least squares. A discussion of the nature of shrinkage is included.

提高 Student t 置信区间的置信陈述AI 解读

The usual confidence interval, based on Student's $t$ distribution, has conditional confidence that is larger than the nominal confidence level. Although this fact is known, along with the fact that increased conditional confidence can be used to improve a confidence assertion, the confidence assertion of Student's $t$ interval has never been critically examined. We do so here, and construct a confidence estimator that allows uniformly higher confidence in the interval and…

正态方差的改进不变置信区间AI 解读

Confidence intervals for the variance of a normal distribution with unknown mean are constructed which improve upon the usual shortest interval based on the sample variance alone. These intervals have guaranteed coverage probability uniformly greater than a predetermined value $1-\alpha$ and have uniformly shorter length. Using information relating the size of the samples mean to that of the sample variance, we smoothly shift the usual minimum length interval closer to zero,…