While database marketers collect vast amounts of customer transaction data, its utilization to improve marketing decisions presents problems. Marketers seek to extract relevant information from large databases by identifying signi6cant variables and prospective customers. In small databases, they could calibrate logistic regression models via maximum-likelihood methods to determine signi6cant v...

In this paper, new aspects of treatment comparison are brought out via the dimension reduction model of Li (1991) for general regression settings. Denoting the treatment indicator by Z and the covariate by X, the model Y = g(v0X + Z; ) is discussed in detail. Estimates of v and are obtained without assuming a functional form for g. Our method is based on the use of SIR (sliced inverse regressio...

Nuclear steam generators are subject to clogging of their internal parts which causes safety issues. Diagnosis methodologies are needed to optimize maintenance operations. Clogging alters the dynamic behaviour of steam generators and particularly the response of the wide range level (WRL – a pressure measurement) to power transients. A numerical model of this phenomenon has previously been deve...

AMS subject classifications: 62H12 62G20 Keywords: Covariates missing at random Inverse selection probability Multi-index model Single-index model a b s t r a c t This paper considers estimation of the semiparametric multi-index model with missing covariates at random. A weighted estimating equation is suggested by invoking the inverse selection probability approach, and estimators of the indic...

Linearity, sometimes jointly with constant variance, is routinely assumed in the context of sufficient dimension reduction. It is well understood that, when these conditions do not hold, blindly using them may lead to inconsistency in estimating the central subspace and the central mean subspace. Surprisingly, we discover that even if these conditions do hold, using them will bring efficiency l...

In this paper, we propose a dimension reduction model for spatially dependent variables. Namely, we investigate an extension of the inverse regression method under strong mixing condition. This method is based on estimation of the matrix of covariance of the expectation of the explanatory given the dependent variable, called the inverse regression. Then, we study, under strong mixing condition,...

Variable selection is an important tool in QSAR. In this article, we employ three known techniques: sliced inverse regression (SIR), principal components regression (PCR) and partial least squares regression (PLSR) for models to predict the boiling points of 530 saturated hydrocarbons. With 122 topological indices as input variables our results show that these three methods have good performanc...

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to lar...

In several dimension reduction techniques, the original variables are replaced by a smaller number of linear combinations. The coefficients of these linear combinations are typically the elements of the left singular vectors of a random matrix. We derive the asymptotic distribution of the left singular vectors of a random matrix that has a normal limit distribution. This result is then used to ...

In this paper, we systematically study the consistency of sliced average variance estimation (SAVE). The findings reveal that when the response is continuous, the asymptotic behavior of SAVE is rather different from that of sliced inverse regression (SIR). SIR can achieve √ n consistency even when each slice contains only two data points. However, SAVE cannot be √ n consistent and it even turns...