In recent years, some researches have been done on simultaneous monitoring of multivariate process mean vector and covariance matrix. However, the effect of measurement error, which exists in many practical applications, on the performance of these control charts is not well studied. In this paper, the effect of measurement error with linearly increasing variance on the performance of ELR contr...

When modeling covariate measurement error in regression, it is typically assumed that the measurement error acts in an additive or multiplicative manner on the true covariate value. However, such assumptions do not hold for the measurement error of sleep-disordered breathing (SDB). The true covariate is the severity of SDB, and the observed surrogate covariate is the number of breathing pauses ...

We introduce a new dimension-reduction technique, the Partially Linear Reduced-rank Regression (PLRR) model, for exploring possible nonlinear structure in a regression involving both multivariate response and covariate. The PLRR model specifies that the response vector loads linearly on some linear indices of the covariate, and nonlinearly on some other indices of the covariate. We give a set o...

1. Observation network data comprise animal presences detected by observer stations at fixed spatial locations. Statistical analysis of these data is complicated by spatial bias in sampling and temporal variability in detection conditions. Advanced methods for analysis of these data are required but are currently underdeveloped. 2. We propose a state-space model for observation network data to ...

An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate (WW ) is a linear function of the unobserved true covariate (X) plus other covariates (Z) in the regression model. In this paper, we consider models for W that include interactions between X and Z. ...