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协方差增强判别分析AI 解读

Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced…

结合非参数协方差估计的纵向数据高效半参数回归AI 解读

For longitudinal data, when the within-subject covariance is misspecified, the semiparametric regression estimator may be inefficient. We propose a method that combines the efficient semiparametric estimator with nonparametric covariance estimation, and is robust against misspecification of covariance models. We show that kernel covariance estimation provides uniformly consistent estimators for the within-subject covariance matrices, and the semiparametric profile estimator…

二元生存数据正态变换模型的半参数极大似然估计AI 解读

We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator…

惩罚样条的渐近性质AI 解读

We study the asymptotic behaviour of penalized spline estimators in the univariate case. We use B-splines and a penalty is placed on mth-order differences of the coefficients. The number of knots is assumed to converge to infinity as the sample size increases. We show that penalized splines behave similarly to Nadaraya--Watson kernel estimators with ‘equivalent’ kernels depending upon m. The equivalent kernels we obtain for penalized splines are the same as those found by…

聚类生存数据脆弱性模型中的协变量测量误差AI 解读

We propose a new class of frailty measurement error models for clustered survival data when covariates are measured with error. We show that the induced hazard function conditional on the observed covariates also follows a frailty model but of a more complicated form. We study the asymptotic bias in regression coefficients and variance components when measurement error is ignored, and the impact of censoring on this asymptotic bias. We show that the naive estimator of the…