Observational studies assessing causal or non-causal relationships between an explanatory measure and an outcome can be complicated by hosts of confounding measures. Large numbers of confounders can lead to several biases in conventional regression based estimation. Inference is more easily conducted if we reduce the number of confounders to a more manageable number. We discuss use of sufficien...

We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are ...

Although the concept of sufficient dimension reduction has been proposed for a long time, studies in the literature have largely focused on properties of estimators of dimension-reduction subspaces in the classical “small p, and large n” setting. Rather than the subspace, this paper considers directly the set of reduced predictors, which we believe are more relevant for subsequent analyses, and...

A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410–428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. ...

Anchor modeling technique has been shown to be useful in reducing computational complexity for speaker identification and indexing of large audio database. In this technique, speakers are projected onto a talker space spanned by a set of predefined anchor models which are usually represented by Gaussian Mixture Models (GMMs). The characterization of each speaker involves calculation of likeliho...

In realizing a speech recognition system robust to variation of speakers, an efficient adaptation algorithm is needed. Most adaptation techniques require many adaptation data to carry out an adaptation task. Adaptation data are often collected from the actual speaker itself in several utterances. With the time needed to gather and transcribe the adaptation utterances, together with the actual e...

One of the most basic characterizations of the relationship between two random variables, X and Y, is the value of their mutual information. Unfortunately, calculating it analytically and estimating it empirically are often stymied by the extremely large dimension of the variables. One might hope to replace such a high-dimensional variable by a smaller one that preserves its relationship with t...

The debate between “structural” and “reduced-form”approaches has generated substantial controversy in applied economics. This article reviews a recent literature in public economics that combines the advantages of reduced-form strategies –transparent and credible identi…cation –with an important advantage of structural models – the ability to make predictions about counterfactual outcomes and w...

The restricted maximum likelihood (REML) estimator of the dispersion matrix for random coefficient models is rewritten in terms of the sufficient statistics of the individual regressions.

Existing model-free tests of the conditional coordinate hypothesis in sufficient dimension reduction (Cook (1998) [3]) focused mainly on the first-order estimation methods such as the sliced inverse regression estimation (Li (1991) [14]). Such testing procedures based on quadratic inference functions are difficult to be extended to second-order sufficient dimension reduction methods such as the...