Here’s What Statistical Science Research Looks Like
Our talented group of faculty and graduate students are producing innovative research in the fields of statistical science and biostatistics.
Check out these samples of faculty and graduate student research to get a sense of the type of work you could do as a graduate student in statistical science.
Two Special Statistical Science Research Projects Taking Place at SMU
1. Geometric and Topological Data Analysis — A defining characteristic of many modern data applications is their unstructured nature. The basic unit of analysis could be something other than a traditional observation, such as regular arrays with fixed numbers of rows and columns and a single observation in each cell. Such questions are not amenable to traditional statistical procedures based on simple array-structured data. Geometric and topological data analysis provides a mathematical representation of the shape of data and extracts structural information from a complex data set. We have developed statistical approaches for geometric and topological data analysis that provide a direct inference on the shape of data.
— Dr. Chul Moon, Assistant Professor
2. Mixed-Value Time Series Analysis — Multivariate time series are routinely modeled and analyzed by the well-known vector autoregressive (VAR) models. The main reasons are ease in computation arising from the imposed linearity, easily understood by a wide audience, and provide predictions. Though VAR models are well understood from a theoretical and methodological point of view, and are quite useful for analysis of continuous-valued data, they are inappropriate when dealing with multivariate time series when some of its components are integer-valued such as the daily number of new patient admissions to a hospital, the number of crimes in a particular region, trading value during a time period. The goal is to develop new statistical tools and models for analyzing multivariate mixed-valued time series data. This is significant because multivariate time series data, discrete and continuous-valued, is collected in diverse scientific areas such as demography, econometrics, sociology, public health and neurobiology for the purpose of forecasting, planning and informing policy.
The group is also investigating measurement error issues using methods such as SIMEX to correct for the bias of regression coefficients in models in which some of the predictor variables are measured with error. The motivation for this work came from attempts to use teacher intervention fidelity measures as predictors of their student performance. Since teacher fidelity is measured by sampling instruction time, it is measured with error. Professors Stokes, Harris, and Cao are co-investigators in a series of jointly funded projects with SMU's Institute for Reading Research. Several Department of Statistical Science graduate students are supported by work in the data laboratory of the institute managing and analyzing data for large-scale reading intervention programs.
— Raanju Sundararajan, Assistant Professor, Department of Statistical Science