This paper studies predictor functions motivated by maximizing a measure of agreement with the predictand. Specifically, it examines distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP), the linear predictor maximizing Lin’s concordance correlation coefficient (CCC) between the predictor and the predictand. It is compared and contrasted, theoretically and through computer experiments, with the estimated least-squares linear predictor (LSLP), with respect to some performance measures. The impact of the plug-in approach, where unknown parameters are replaced by estimators in forming the predictors, is also examined. Finite-sample and asymptotic properties are obtained, and confidence intervals and prediction intervals are also presented. Predictors are illustrated using two real data sets: an eye data set and a body fat data set. Results indicate that the estimated MALP is a viable alternative to the estimated LSLP if one desires a predictor whose predicted values possess higher agreement with the predictand values, as measured by the CCC. The proposed predictor could be used, for instance, in missing value settings where imputation is needed; or in calibration problems where the goal is to have good agreement between measures obtained via a gold standard method, but which could be expensive and/or hard-to-implement, and those obtained through alternative cheaper and/or easier-to-implement methods.

The analysis of functional data is an increasingly relevant part of statistics. The exploratory data analytic method of cluster analysis plays an important role in different fields, including those in which functional data are collected. To improve the accuracy when clustering functional data, we propose a predictive likelihood function which serves as an objective function to be optimized in order to determine the most appropriate clusters. To optimize the objective function over the space of clustering partitions, we produce a Markov chain of partitions. At each step, we generate via a simulated annealing algorithm new partitions for which the predictive likelihood is evaluated. A schedule for adjusting the temperature parameter enhances the efficiency of the chain. Compared with a functional K-means algorithm and the algorithm of the R function funFEM, our method can achieve higher performance as measured by the Rand index on simulated data. Our approach is also applied to two real data sets, a vertical density profile data set and a yeast gene data set, and it achieves meaningful clustering results.

When two random variables are positive quadrant dependent (PQD), they are more likely to assume small (or large) values simultaneously compared with when the random variables are independent. This de...

BMC Medical Research Methodology - Inverse probability weighting (IPW) is a widely used method to estimate the causal effect of an exposure on the outcome of interest in epidemiological...

This link will take you to a page that’s not on LinkedIn