Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Mi-Ok Kim

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest, while the random coefficients represent cluster…


Lianming Wang

Interval-censored data naturally arise in many fields such as aids clinical trial studies and follow-up medical studies. The main feature is that the failure time of interest is not observed exactly but is known to fall within some interval. Regression analysis on interval-censored data is challenging due to the complex data likelihood and the censoring mechanism producing such data. In this talk, I will review the commonly used semiparametric…


Ana-Maria Staicu

We introduce a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial location. Such data are not envisaged by the current approaches to model functional data, due to the lack of Gaussian – like features. Our methodology allows modeling the pointwise quantiles, has interpretability advantages and is computationally feasible. Our methods were motivated by and are illustrated with a…


MoonJung Cho

In applied work with generalized variance function models for sample survey data, one generally seeks to develop and validate a model that is relatively parsimonious and that produces variance estimators that are approximately unbiased and relatively stable. This development and validation work often begins with regression of initial variance estimators (computed through standard design-based methods) on one or more candidate explanatory…


William A. Brenneman

Modern industry is constantly seeking to efficiently produce new and improved products. Statisticians play a central role in helping the product team quickly identify areas for improvement and optimization. Many of the problems faced in industry can be solved with known statistical methods, while occasionally there are problems encountered that require original research. For a research statistician practicing in industry, these types of problems…


Somnath Datta

We will consider inference for various marginal temporal functions of a multistate system such as the state occupation probabilities, the integrated transition hazards, the state entry, exit and sojourn time distributions. For most parts, we will not assume a Markov or semi-Markov system. Nonparametric estimators under right censored, current status and interval censored data will be constructed. In this talk, we will consider construction of…


Lixing Zhu

For a general single-index model that does not assume an additive structure of unknown regression function and error with the dimension of predictor vector larger than the sample size, the consistency of predictor selection and estimation has not yet been investigated in the literature. In this paper, we investigate this issue by the following methods. First we formulate the index in the sufficient dimension reduction framework motivated by the…


Dennis K.J. Lin

In the past decades, we have witnessed the revolution of information technology. Its impact to statistical research is enormous. This talk attempts to address recent developments and some potential research issues in Business, Industry and Government (BIG) Statistics, with special focus on computer experiment and information systems. An overall introduction and review will be given, followed by specific research potentials. For each subject, the…


Peter McCullagh

This lecture is concerned with probability models for distance matrices, which are non-negative symmetric matrices of negative type. Several families of distributions are considered, including Wishart distance matrices and Mahalanobis distance matrices, all derived ultimately from Gaussian matrices by marginalization. The likelihood functions are obtained in a relatively straightforward manner without an explicit representation of the joint…


Laine Bradshaw

Commonly utilized in educational testing, models within the unidimensional item response theory (IRT) framework locate a student’s overall ability along a latent continuum by modeling the response probabilities to a set of test items as a function of a single continuous latent variable. Diagnostic classification models (DCMs) are an emerging class of models that, in contrast to IRT models, identify the separate components of what students know…
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