Ye, Lin, and Taylor (2008, Biometrics 64, 1238-1246) proposed a joint model for longitudinal measurements and time-to-event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two-stage regression calibration approach that is simpler to implement than a joint modeling approach...

The purpose of this study was to develop regression equations predicting torque output throughout the range of motion for the human elbow, shoulder, knee, and hip. Twenty-two healthy males participated. Torque values throughout the sagittal plane range of motion (i.e., flexion and extension) of the right elbow, shoulder, knee and hip were recorded (isokinetic dynamometer, 1 rad/sec) and express...

the rank-k numerical range has a close connection to the construction of quantum error correction code for a noisy quantum channel. for noisy quantum channel, a quantum error correcting code of dimension k exists if and only if the associated joint rank-k numerical range is non-empty. in this paper the notion of joint rank-k numerical range is generalized and some statements of [2011, generaliz...

Fast multi-output relevance vector regression (FMRVR) algorithm is developed for simultaneous estimation of groundwater and lake water depth the first time in this study. The FMRVR a analysis technique which can simultaneously predict multiple outputs multi-dimensional input. data used study collected from 34 stations located Urmia basin over 40-year period. performance model examined contrast ...

There is a growing demand for multiple output prediction methods capable of both minimizing residual errors and capturing the joint distribution of the response variables in a realistic and consistent fashion. Unfortunately, current methods are designed to optimize one of the two criteria, but not both. This paper presents a framework for multiple output regression that preserves the relationsh...

The goal of this paper is to address the issue of non linear regression with outliers possibly in high dimension, without specifying the form of the link function and under a parametric approach. Non linearity is handled via an underlying mixture of affine regressions. Each regression is encoded in a joint multivariate Student distribution on the responses and covariates. This joint modelling a...

This paper studies the connections among quantile regression, the asymmetric Laplace distribution, maximum likelihood and maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. Using the resulting score functions we propose an estimator bas...

is called the regression function (of Y on X). The basic goal in nonparametric regression is to construct an estimate f̂ of f0, from i.i.d. samples (x1, y1), . . . (xn, yn) ∈ R × R that have the same joint distribution as (X,Y ). We often call X the input, predictor, feature, etc., and Y the output, outcome, response, etc. Importantly, in nonparametric regression we do not assume a certain param...

The authors derive the joint distributions of a studentized deleted residual and various regression quantities, calculated with all the data or with one case deleted. They show that the correlation between the studentized deleted residual and the deleted test statistic has an interesting interpretation in terms of well-known regression quantities. These results allow them to examine the effect ...