stats 600 umich
; stochastic difference equations autoregressive schemes, moving averages; large sample inference and prediction; covariance structure and spectral densities; hypothesis testing and estimation and applications and other topics. Concrete examples of homology, gene finding, structure analysis. Recent developments in the foundations and methodology of sampling finite populations. (4 Credits). Statistics 547: Probabilistic Modeling in Bioinformatics (Math 547), Probabilistic models of proteins and nucleic acids. Statistics 580: Methods and Theory of Sample Design (SOC 717/BIOS 617), Theory underlying sample designs and estimation procedures commonly used in survey practice. Topics include estimation and inference, diagnostics, model selection, and interpretation of results associated with linear models and general linear models. (3 Credits). Graduate standing. linear models and generalized estimating equations (GEE); (4) 1. The h�bbd```b``� "��H&0�"�t���������d�|&Cq�����&{�H�`6�8#X���zR��o>�8���`�z0�&�I!0� This course covers topics in large sample theory that are central for statistical inference, including: (1) modes of convergence, central limit theorems for averages and medians, and asymptotic relative efficiency; (2) estimating equations including the law of large numbers for random functions, consistency and asymptotic normality for maximum likelihood and M-estimators, the E-M algorithm, and asymptotic confidence intervals; (3) large sample theory for likelihood ratio tests. estimation, with some discussion of non least-squares approaches; (2) X���C ��A1C(?��%��l�>1�1�`~�o �`�iS=�!� Problem sets 2019/09/13. Pre-requisites: MATH 417 and either STATS 611 or BIOSTAT 602. This includes: the theory and practice of testing hypotheses, statistical estimation theory, the basic statistical theory underlying the linear model, an introduction to econometric methods, and the nature of the difficulties which arise in applying statistical procedures to economic research problems. Advisory prerequisites: MATH 451, STATS 425, STATS 426. course is fast-paced, and focuses on the motivation, construction, and (3 Credits). Pre-requisites: MATH 451, STATS 425, and STATS 426 or equivalent courses in probability, statistics and real analysis. Pre-requisites: MATH 597. Topics vary by instructor. (3 Credits), Statistics 503: Statistical Learning II: Multivariate Analysis, The course covers methods for modern multivariate data analysis and statistical Students will be expected to complete a course project and present to group. Selected topics in theoretical statistics. Theoretical Statistics (at the level of Stats 426 or equivalent), Generalized linear models including logistics regression, Poisson regression, contingency tables. This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. This course introduces students to the theory of statistical inference. The response variable could be continuous, binary or counts. Knowledge of probability at the level of BIOSTAT 601 or MATH 525. Algorithms for sequence alignment, statistical analysis of similarity scores, hidden Markov models, neural networks training, gene finding, protein family profiles, multiple sequence alignment, sequence comparison and structure prediction. Regular attendance at the lecture and lab is expected. (3 Credits). Graduate standing. This course is designed to acquaint students with classical papers in mathematics and applied statistics and probability theory, to encourage them in critical independent reading and to permit them to gain pedagogical experience during the course of their graduate training. Visiting researchers will provide a brief account of their aims and data before defining the methodological challenge for which they desire discussion.
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