论文检索

连续谱回归：以交叉验证统一最小二乘、偏最小二乘与主成分回归AI 解读

SUMMARY The paper addresses the evergreen problem of construction of regressors for use in least squares multiple regression. In the context of a general sequential procedure for doing this, it is shown that, with a particular objective criterion for the construction, the procedures of ordinary least squares and principal components regression occupy the opposite ends of a continuous spectrum, with partial least squares lying in between. There are two adjustable ‘parameters’…

关于优势比的贝叶斯推断中的最优样本分配AI 解读

The choice of ratio of sample sizes of two groups of binary observations for comparing two proportions is studied using a Bayesian approach. Prior uncertainty about the unknown proportions thus enters the design calculations.

优势比贝叶斯推断的最优样本分配AI 解读

The choice of ratio of sample sizes of two groups of binary observations for comparing two proportions is studied using a Bayesian approach. Prior uncertainty about the unknown proportions thus enters the design calculations.

摘要暂缺

删失导致的信息损失AI 解读

The expected gain in information provided by an experiment is used to investigate the loss of information when observations on lifetimes in reliability and survival studies are censored. It is assumed that the lifetime distribution is exponential. The loss of information through only obtaining data at given time points is also considered.

基于秩序统计量的两样本问题贝叶斯分析AI 解读

Summary A Bayesian analysis of the two-sample, location-shift problem based on the rank order statistic is described. It is assumed that the data available can only at best be considered as being on the ordinal scale. Exact and simplified approximate procedures are suggested. These are not distribution-free since the underlying population distributions need to be specified.

用于线性回归控制的最优回归设计AI 解读

A Bayesian decision theory approach to the choice of regression design is considered when it is intended to use the regression to help control the dependent variable at a chosen value, the control to be effected by choosing certain of the independent variables and fixing them at selected values.

用于控制的线性回归最优设计AI 解读

A Bayesian decision theory approach to the choice of regression design is considered when it is intended to use the regression to help control the dependent variable at a chosen value, the control to be effected by choosing certain of the independent variables and fixing them at selected values.

Lehmann 备择下两样本问题的贝叶斯分析AI 解读

A Bayesian analysis of the two-sample problem under the Lehmann alternatives is considered. This analysis is based on the rank order statistic. Comparisons are made with Savage's rank test.

线性回归预测中的实验选择AI 解读

Bayesian decision theory is used to obtain optimal designed and random regression experiments. If a choice is possible, it is shown how to decide between them. This is done if the data obtained are to be analysed in order that a future values of the dependent variable can be predicted using the best subset of the independent variables.

Lehmann 备择下两样本问题的贝叶斯分析AI 解读

A Bayesian analysis of the two-sample problem under the Lehmann alternatives is considered. This analysis is based on the rank order statistic. Comparisons are made with Savage's rank test.

以线性回归预测为目标的实验选择AI 解读

Bayesian decision theory is used to obtain optimal designed and random regression experiments. If a choice is possible, it is shown how to decide between them. This is done if the data obtained are to be analysed in order that a future values of the dependent variable can be predicted using the best subset of the independent variables.

曲线型多元回归中用于预测的变量选择AI 解读

A Bayesian formulation of the problem of analysing data from a curvilinear regression of $y$ on $x_1, x_2, \cdots, x_r$ in order to predict a future value of $y$ is considered. The problem is to obtain a criterion to decide which is the best subset of $x_1, x_2, \cdots, x_r$ to perform this prediction. Under very strict assumptions the criterion obtained is shown to use the same statistic as the orthodox (least squares) approach.

最优回归设计的决策理论方法AI 解读

A decision theory approach to the problem of finding an optimal regression design is considered when the data obtained through using this design are to be analyzed in order that a future value of the dependent variable can be predicted and also the best subset of independent variables to use for the prediction determined.

最优回归设计的决策理论方法AI 解读

A decision theory approach to the problem of finding an optimal regression design is considered when the data obtained through using this design are to be analyzed in order that a future value of the dependent variable can be predicted and also the best subset of independent variables to use for the prediction determined.