The weighted least absolute deviation (WLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to achieve robust parameter estimation and variable selection in regression simultaneously. Compared with the LAD-LASSO method, the...

A geographical weighted empirical best linear unbiased predictor (GWEBLUP) for a small area average is proposed, and an estimator of its conditional mean squared error is developed. The popular empirical best linear unbiased predictor under the linear mixed model is obtained as a special case of...

A binary unsupervised classification problem where each observation is associated with an unobserved label that needs to be retrieved is considered. More precisely, it is assumed that there are two groups of observation: normal and abnormal. The ‘normal’ observations are coming from a known...

The spatial scan statistic (SaTScan) has become one of the most popular methods for detecting and evaluating spatial clusters. However, this method can only identify circular or elliptical clusters and is not a good fit for the detection of irregularly shaped clusters. Numerous methods have...

Risk factor models in clinical epidemiology are important for identifying individuals at high risk of poor health outcomes and for guiding intervention strategies to reduce risk. Such models are often based on generalised linear models (GLM) with a multiplicative risk, rate or odds assumption....

A paired data set is common in microarray experiments, where the data are often incompletely observed for some pairs due to various technical reasons. In microarray paired data sets, it is of main interest to detect differentially expressed genes, which are usually identified by testing the...

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related....

A new estimation procedure for the Cox proportional hazards model is introduced. The method proposed employs the sample covariance matrix of model covariates and alternates between estimating the baseline cumulative hazard function and estimating model coefficients. It is shown that the...

The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust regression rely on selecting numerous subsamples of the data. New algorithms for LMS and LTS estimators that have increased computational efficiency due to improved combinatorial sampling are...

Statistical inference in censored quantile regression is challenging, partly due to the unsmoothness of the quantile score function. A new procedure is developed to estimate the variance of the Bang and Tsiatis inverse-censoring-probability weighted estimator for censored quantile regression by...