Department of Statistics - Texas A&M University

1 Department of Statistics 1 Department OF : V. JohnsonThe Department of Statistics offers a graduate program leading to thedegrees of Master of Science or Doctor of Department of Statistics has two master s degree programs, MS inStatistics and MS in , Derya G, Instructional Associate ProfessorStatisticsPHD, Texas A&M University , 1996 Bhattacharya, Anirban, Assistant ProfessorStatisticsPHD, Duke University , 2012 Carroll, Raymond J, Distinguished ProfessorStatisticsPHD, Purdue University , 1974 Chen, Willa W, ProfessorStatisticsPHD, New York University , 2000 Cline, Daren B, ProfessorStatisticsPHD, Colorado State University , 1983 Dabney, Alan R, Associate ProfessorStatisticsPHD, University of Washington, 2006 Gaynanova, Irina, Assistant ProfessorStatisticsPHD, Cornell University , 2015 Hart, Jeffrey D, ProfessorStatisticsPHD, Southern Methodist University , 1981 Hernandez Magallanes, Irma Del Consue, Visiting Assistant ProfessorStatisticsPHD, University California Berkley.

2 2010 Huang, Jianhua, ProfessorStatisticsPHD, University of California, Berkeley, 1997 Johnson, Valen E, Distinguished ProfessorStatisticsPHD, University Of Chicago, 1989 Jones, Edward R, Executive ProfessorStatisticsPHD, Virginia Tech, 1976 Jun, Mikyoung, Associate ProfessorStatisticsPHD, University of Chicago, 2005 Katzfuss, Matthias S, Assistant ProfessorStatisticsPHD, The Ohio State University , 2011 Kolodziej, Elizabeth Y, Instructional Assistant ProfessorStatisticsPHD, Texas A&M University , 2010 Liang, Hwa Chi, Instructional Assistant ProfessorStatisticsPHD, University of New Mexico, 2003 Long, James P, Assistant ProfessorStatisticsPHD, University of California - Berkeley, 2013 Longnecker, Michael T, ProfessorStatisticsPHD, Florida State University , 1976 Mallick, Bani K, Distinguished ProfessorStatisticsPHD, University of Connecticut, 1994 Mueller-Harknett, Ursula U, ProfessorStatisticsPHD, Universitat Bremen, Germany, 1997 Newton, Howard J, Senior ProfessorStatisticsPHD, SUNY Buffalo, 1975 Pati, Debdeep, Associate ProfessorStatisticsPHD, Duke University , 2012 Pourahmadi, Mohsen, ProfessorStatisticsPHD, Michigan State University , 1980 Sang, Huiyan, Associate ProfessorStatisticsPHD, Duke University , 2008 Schmiediche, Henrik, Instructional Associate ProfessorStatisticsPHD, Texas A&M University , 1993 Sheather, Simon J, ProfessorStatisticsPHD, La Trobe University , 1986 Sinha, Samiran, ProfessorStatisticsPHD, University of Florida, 2004 Spiegelman, Clifford H, Distinguished ProfessorStatisticsPHD, Northwestern University , 1976 Subbarao, Suhasini T, ProfessorStatisticsPHD, University of Bristol, 20012 Department of StatisticsWang, Suojin, ProfessorStatisticsPHD, University of Texas at Austin, 1988 Wehrly, Thomas E, Senior ProfessorStatisticsPHD, University of Wisconsin - Madison, 1976 Wong, Ka Wai, Assistant ProfessorStatisticsPHD.

3 University California, Davis, 2014 Zhang, Xianyang, Assistant ProfessorStatisticsPHD, University of Illinois at Urbana - Champaign, 2013 Zhou, Lan, Associate ProfessorStatisticsPHD, University of California, Berkeley, 1997 Masters Master of Science in Analytics ( ) Master of Science in Statistics ( )Doctoral Doctor of Philosophy in Statistics ( )Certificates Applied Statistics Certificate ( )CoursesSTAT 601 Statistical AnalysisCredits 4. 3 Lecture Hours. 2 Lab students in engineering, physical and mathematical to probability, probability distributions and statisticalinference; hypotheses testing; introduction to methods of analysis suchas tests of independence, regression, analysis of variance with someconsideration of planned : MATH 152 or MATH 604 Topics in Statistical ComputationsCredits 3. 3 Lecture uses of existing statistical computer programs (SAS, R, etc.);generation of random numbers; using and creating functions andsubroutines; statistical graphics; programming of simulation studies; anddata management : MATH 221, MATH 251, or MATH 605 Advanced Statistical ComputationsCredits 3.

4 3 Lecture languages, statistical software and computingenvironments; development of programming skills using modernmethodologies; data extraction and code management; interfacing lower-level languages with data analysis software; simulation; MC integration;MC-MC procedures; permutation tests; : STAT 612 and STAT 607 SamplingCredits 3. 3 Lecture , execution and analysis of sampling from finite populations;simple, stratified, multistage and systematic sampling; ratio : STAT 601 or STAT 652 or concurrent enrollment inSTAT 608 Regression AnalysisCredits 3. 3 Lecture , curvilinear, nonlinear, robust, logistic and principal componentsregression analysis; regression diagnostics, transformations, analysis : STAT 601 or STAT 610 Theory of Statistics - Distribution TheoryCredits 3. 3 Lecture introduction to probability theory; distributions and expectationsof random variables, transformations of random variables and orderstatistics; generating functions and basic limit : MATH 409 or concurrent enrollment in MATH 611 Theory of Statistics - InferenceCredits 3.

5 3 Lecture of estimation and hypothesis testing; point estimation, intervalestimation, sufficient Statistics , decision theory, most powerful tests,likelihood ratio tests, chi-square : STAT 610 or 612 Theory of Linear ModelsCredits 3. 3 Lecture algebra for statisticians; Gauss-Markov theorem; estimability;estimation subject to linear restrictions; multivariate normal distribution;distribution of quadratic forms; inferences for linear models; theory ofmultiple regression and AOV; random-and mixed-effects : Course in linear 613 Statistical Methodology ICredits 3. 3 Lecture of likelihood inference; exponential family models; grouptransformation models; survival data; missing data; estimation andhypotheses testing; nonlinear regression models; conditional andmarginal inferences; complex models-Markov chains, Markov randomfields, time series, and point : STAT 614 Probability for StatisticsCredits 3. 3 Lecture and measures; expectation and integrals, Kolmogorov'sextension theorem; Fubini's theorem; inequalities; uniform integrability;conditional expectation; laws of large numbers; central limit theoremsPrerequisite: STAT 610 or its 615 Stochastic ProcessesCredits 3.

6 3 Lecture of the theory of stochastic processes; includes countable-stateMarkov processes, birth-death processes, Poisson point processes,renewal processes, Brownian motion and diffusion processes andcovariance-stationary processes; theoretical development andapplications to real world : STAT 610; MATH of Statistics 3 STAT 616 Statistical Aspects of machine learning I: ClassicalMultivariate MethodsCredits 3. 3 Lecture methods from traditional multivariate analysis and variousextensions; probability distributions of random vectors and matrices,multivariate normal distributions, model assessment and selection inmultiple regression, multivariate regression, dimension reduction, lineardiscriminant analysis, logistic discriminant analysis, cluster analysis,multidimensional scaling and distance geometry, and : STAT 612, STAT 618 Statistical Aspect of machine learning II: Modern TechniquesCredits 3. 3 Lecture course in statistical machine learning ; recursive partition andtree-based methods, artificial neural networks, support vector machines,reproducing kernels, committee machines, latent variable methods,component analysis, nonlinear dimensionality reduction and manifoldlearning, matrix factorization and matrix completion, statistical analysisof tensors and multi-indexed : STAT 612, STAT 613, and STAT 620 Asymptotic StatisticsCredits 3.

7 3 Lecture of basic concepts and important convergence theorems;elements of decision theory; delta method; Bahadur representationtheorem; asymptotic distribution of MLE and the LRT Statistics ;asymptotic efficiency; limit theory for U- Statistics and differentialstatistical functionals with illustrations from M-,L-,R-estimation; : STAT 621 Advanced Stochastic ProcessesCredits 3. 3 Lecture expectation; stopping times; discrete Markov processes;birth-death processes; queuing models; discrete semi-Markov processes;Brownian motion; diffusion processes, Ito integrals, theorem and limitdistributions; differential statistical functions and their limit distributions;M-,L-, : STAT 614 or STAT 623 Statistical Methods for ChemistryCredits 3. 3 Lecture topics of process optimization, precision and accuracy;curve fitting; chi-squared tests; multivariate calibration; errors incalibration standards; Statistics of : STAT 601, STAT 641 or STAT 652 or approval of 624 Databases and Computational Tools Used in Big DataCredits 3.

8 3 Lecture of common tools used by statisticians for high performancecomputing and big data type problems; shell scripting; HPC clusters;code optimization and vectorization; parallelizing applications usingnumerical libraries; open MP, MPI and parallel R; data management andrevision control using Git; exploration of SQL, survey NOSQL databases;introduction to : Knowledge of R, Fortran, or 626 Methods in Time Series AnalysisCredits 3. 3 Lecture to statistical time series analysis; autocorrelation andspectral characteristics of univariate, autoregressive, moving averagemodels; identification, estimation and : STAT 601 or STAT 642 or approval of 627 Nonparametric Function EstimationCredits 3. 3 Lecture function estimation; kernel, local polynomials, Fourierseries and spline methods; automated smoothing methods includingcross-validation; large sample distributional properties of estimators;recent advances in function : STAT 630 Overview of Mathematical StatisticsCredits 3.

9 3 Lecture probability theory including distributions of random variables andexpectations. Introduction to the theory of statistical inference fromthe likelihood point of view including maximum likelihood estimation,confidence intervals, and likelihood ratio tests. Introduction to : MATH 221, MATH 251, and MATH 631 Statistical Methods in FinanceCredits 3. 3 Lecture and the capital asset pricing model, Statistics for portfolioanalysis, resampling, time series models, volatility models, option pricingand Monte Carlo methods, copulas, extreme value theory, value at risk,spline smoothing of term : STAT 610, STAT 611, STAT 632 Statistical Methodology II-Bayesian Modeling and InferenceCredits 3. 3 Lecture theory; fundamentals of Bayesian inference; single and multi-parameter models; Gaussian model; linear and generalized linear models;Bayesian computations; asymptotic methods; non-iterative MC; MCMC;hierarchical models; nonlinear models; random effect models; survivalanalysis; spatial : STAT 633 Advanced Bayesian Modeling and ComputationCredits 3.

10 3 Lecture methods in their research; methodology, and applications ofBayesian methods in bioinformatics, biostatistics, signal processing, machine learning , and related : STAT 608, STAT 613, STAT 636 Applied Multivariate AnalysisCredits 3. 3 Lecture extension of the chi-square and t-tests, discriminationand classification procedures; applications to diagnostic problems inbiological, medical, anthropological and social research; multivariateanalysis of variance, principal component and factor analysis, : MATH 304, STAT 638 Introduction to Applied Bayesian MethodsCredits 3. 3 Lecture regarding parameters and how they can be explicitlydescribed as a posterior distribution which blends information froma sampling model and prior distribution; emphasis on modeling andcomputations under the Bayesian paradigm; includes prior distributions,Bayes Theorem, conjugate and non-conjugate models, posteriorsimulation via the Gibbs sampler and MCMC, hierarchical : STAT 630, or equivalent or approval of Department of StatisticsSTAT 639 Data Mining and AnalysisCredits 3.