91爆料

BIOST 111 Lectures in Applied Statistics (1) NSc
Weekly lectures illustrating the importance of statisticians in a variety of fields, including medicine and the biological, physical, and social sciences. Credit/no-credit only. Offered: jointly with STAT 111; Sp.

BIOST 290 Introduction to Biomedical Research: Study Design and Interpretation (3) NSc
Introduces biostatistical concepts necessary for the interpretation, evaluation, and communicaiton of biomedical research. Includes biomedical study design, randomization, graphical data displays, control bias, variability, confounding, interaction, and ethics of human experimentation. Students participate in group and individual projects, group discussions, and oral presentations.

BIOST 302 Forensic Genetics (3) NSc/SSc, RSN
Introduces the field of forensic genetics through discussion of genetic and statistical issues emerging since the introduction of DNA profiling. Students develop the skills to interpret the evidence of matching genetic profiles; to understand calculations relevant for parentage determination; the identification of remains; the use of genetic genealogy, and to consider the implications of familial searching of DNA databases. Recommended: GENOME 371 or equivalent. Offered: jointly with PHG 302; Sp.

BIOST 310 Biostatistics for the Health Sciences (4) RSN
Introduction to statistics for students who plan to major in health sciences. Uses case studies from popular and scientific literature to study topics such as data description, study design, estimation, hypothesis testing, regression. Emphasizes concepts and interpretation rather than computation. Students should be able to interpret graphs and use concepts covered in 2nd-year algebra, including linear equations, logarithms, summation notation. Offered: AWSp.

BIOST 311 Regression Methods in the Health Sciences (4) RSN
Introduces regression methods for analysis of continuous, binary, and time-to-event (survival) data. Covers linear regression; logistic regression; and proportional hazards regression, all at an introductory level. Makes use of examples drawn from the biomedical and health sciences literature. Prerequisite: BIOST 310.

BIOST 401 Computational and Applied Genetic Epidemiology (5) RSN
Advanced topics in genetic epidemiology for undergraduate students, focusing on hands-on introduction to computational analysis of population genetics and individual health data using R programs. Students will investigate how genes and environment interact to cause disease and health-states and to inform public health interventions. Recommended: PHG 301 or prior background in basic genetics and statistics. Offered: jointly with EPI 410/PHG 401; Sp.

BIOST 405 Introduction to Health Data Analytics (3)
Covers the process of and tolls used in evaluating data using analytical and logical reasoning to examine each component of the health data. Offered: jointly with HIHIM 405.

BIOST 406 Research Design and Statistics for HIHIM (3)
Explores healthcare and research statistics. Addresses hospital statistics, used to calculate usage levels of heathcare resources and outcomes of clinical operations, and research statistics, used to summarize and describe significant characteristics of a data set, and to make inferences about a population based on data collected from a sample. In addition, principles of research are described, including the Institutional Review Board process. Offered: jointly with HIHIM 425/STAT 406.

BIOST 425 Introduction to Nonparametric Statistics (3) NSc
Overview of nonparametric methods, such as rank tests, goodness of fit tests, 2 x 2 tables, nonparametric estimation. Useful for students with only a statistical methods course background. Prerequisite: Either STAT 311 and STAT 340, STAT 390, or STAT 391. Offered: jointly with STAT 425.

BIOST 499 Undergraduate Research (1-10, max. 30)
Supervised reading programs; library and field research; special projects.

BIOST 502 Introduction to Statistics in Health Sciences (4)
Description and examples of common concepts in biostatistics. Probability, point and confidence interval estimation, hypothesis testing including two-sample and paired t and chi-square tests, introduction to simple linear regression. Emphasizes applications in health sciences. Offered: W.

BIOST 503 Application of Statistics to Health Sciences ([0-3]-, max. 3)
Standard statistical techniques presented with examples drawn from the health sciences literature. Critical interpretation of research results, and introduction to the computer for data processing and statistical analysis. Prerequisite: BIOST 502 or equivalent. Offered: Sp.

BIOST 504 Foundations of Public Health for Biostatistics (2)
Introduces students to key foundational concepts in Public Health and highlights the role of biostatistical methods and applications. Course overlaps with: ENV H 501. Prerequisite: BIOST 522 or STAT 512; and BIOST 514 or BIOST 517 (can be taken concurrently) Credit/no-credit only. Offered: A.

BIOST 505 Writing, Presentation, and Collaboration Skills for Biostatistics (2)
Develops communication and collaboration skills for biostatisticians working as part of a biomedical research team. Topics include effective dialogue with collaborators; summarizing scientific ideas and questions and translating these to targets of inference; collaborating to develop appropriate study designs and statistical analyses; communicating statistical analysis methods and results in clear written summaries and graphical displays. Prerequisite: BIOST 514; and BIOST 515, which may be taken concurrently; recommended: at least one quarter of both mathematical statistics and applied Biostatistics at the graduate level. Credit/no-credit only. Offered: W.

BIOST 507 Health Data Analytics (3)
Healthcare organizations generate a large number of clinical performance metrics. Extracted data from different data systems is used to display relevant metrics in dashboards that are meaningful to senior leadership. Statistical tools may determine if differences in performance are significant and what factors are associated with the differences. Predictive models may then be built and inputs varied to obtain performance improvements. Recommended: beginner Excel skills. Offered: jointly with HIHIM 524; Sp.

BIOST 508 Biostatistical Reasoning for the Health Sciences (4)
Provides a broad overview of biostatistical methods. Students are introduced to the data summaries and presentation, statistical inference (including hypothesis testing, p-values, and confidence intervals), sample size calculation, and modeling approaches such as linear regression analysis. Includes hands-on data analysis. Prerequisite: EPI 511 or instructor's permission Offered: W.

BIOST 509 Introduction to R for Data Analysis in the Health Sciences (2)
Introduction to R for data analysis. Covers installing R; scripts; reading in data and writing output; using help files; using functions; writing functions; graphics; R packages; data manipulation; loops; permutation tests; bootstrapping; and fitting models. Prerequisite: upper-division course in statistics or permission of instructor.

BIOST 510 Biostatistics in Dentistry (3)
Introduction to concepts and methods of descriptive and inferential statistics with applications in dentistry emphasized. Topics include comparison of means and proportions, hypothesis testing, confidence intervals, non-parametric methods, linear regression, and correlation. Prerequisite: enrollment in School of Dentistry or permission of instructor. Offered: jointly with OHS 568; S.

BIOST 511 Medical Biometry I (4)
Presents the principles and methods of data description and elementary parametric and nonparametric statistical analysis. Examples from the biomedical literature, and real data sets are analyzed by the students after a brief introduction to the use of standard statistical computer packages. Statistical techniques covered include description of samples, comparison of two sample means and proportions, simple linear regression and correlation. Offered: A.

BIOST 512 Medical Biometry II (4)
Multiple regression, analysis of covariance, and an introduction to one-way and two-way analyses of variance: including assumptions, transformations, outlier detection, dummy variables, and variable selection procedures. Examples drawn from the biomedical literature with computer assignments using standard statistical computer packages. Prerequisite: BIOST 511 or BIOST 517. Offered: W.

BIOST 513 Medical Biometry III (4)
Analysis of categorical data including two sample methods, sets of 2 x 2 tables, R x C tables, and logistic regression. Classification and discrimination techniques. Survival analysis including product limit estimates and the Cox proportional hazards model. Prerequisite: BIOST 512 or permission of instructor. Offered: Sp.

BIOST 514 Biostatistics I (4)
Presentation of principles and methods of data description; graphics; point, confidence interval estimation; hypothesis testing; relative risk; odds ratio; Mantel-Haenszel; chi-square test. Examples drawn from biomedical literature; real-data sets analyzed using statistical computer packages. Prerequisite: biostatistics majors or permission of instructor. Offered: A.

BIOST 515 Biostatistics II (4)
Introduction to linear models; multiple regression, correlation; residual analysis; dummy variables; analysis of covariance; one-, two-way analysis of variance; randomized blocks; fixed, random effects (repeated measure, factorial designs); multiple comparisons . Real biomedical data sets analyzed. Prerequisite: BIOST 514, biostatistics major, or permission of instructor. Offered: W.

BIOST 516 Statistical Methods in Genetic Epidemiology (3)
Theory and application of statistical techniques used in genetic epidemiology. Includes discussion of association studies, linkages and segregation analyses. Examples stressed with reference to assumptions and limitations. Prerequisite: either BIOST 513 or BIOST 518; PHG 511/EPI 517; or permission of instructor. Offered: jointly with EPI 535/PHG 519.

BIOST 517 Applied Biostatistics I (4)
Introduction to the analysis of biomedical data. Descriptive and inferential statistical analysis for discrete, continuous, and right-censored random variables. Analytic methods based on elementary parametric and non-parametric models for one sample; two sample (independent and paired), stratified sample, and simple regression problems. Offered: A.

BIOST 518 Applied Biostatistics II (4)
Multiple regression for continuous, discrete, and right-censored response variables, including dummy variables, transformations, and interactions. Introduction to regression with correlated outcome data. Model and case diagnostics. Computer assignments using real data and standard statistical computer packages. Prerequisite: BIOST 517 or permission of instructor. Offered: W.

BIOST 519 Advanced Epidemiologic Methods I (3)
First in a series of 2 courses. Increases knowledge of epidemiological principles by introducing methodological approaches to handling common problems in epidemiologic research that extend beyond the scope of traditional methods. Prerequisite: EPI 512 and EPI 513 Offered: jointly with EPI 515.

BIOST 520 Advanced Epidemiologic Methods II (4)
Second of a series of 2 courses whose objective is to deepen students' knowledge of epidemiological principles by introducing methodological approaches to handling common problems in epidemiologic research that extend beyond the scope of traditional methods. Prerequisite: EPI 515. Offered: jointly with EPI 516.

BIOST 522 Statistical Inference for Biometry I (4)
This is the first in a two-course sequence that introduces the theory of statistical inference that provides foundations to common biostatistical methods. Topics of the sequence include basic concepts of probability, parametric distributions, exact and asymptotic sampling distribution of statistics, maximum likelihood estimation, unbiased estimating equations, theory of hypothesis testing and Bayesian inference. Course overlaps with: STAT 512 and STAT 513. Prerequisite: either STAT 395, STAT 421, STAT 423, STAT 504 or BIOST 514 (can be taken concurrently) Offered: A.

BIOST 523 Statistical Inference for Biometry II (4)
This is the second in a two-course sequence that introduces the theory of statistical inference that provides foundations to common biostatistical methods. Topics of the sequence include basic concepts of probability, parametric distributions, exact and asymptotic sampling distribution of statistics, maximum likelihood estimation, unbiased estimating equations, theory of hypothesis testing and Bayesian inference. Course overlaps with: STAT 512 and STAT 513. Prerequisite: BIOST 522, or permission of instructor. Offered: W.

BIOST 524 Design of Medical Studies (3)
Design of medical studies, with emphasis on randomized controlled clinical trials. Bias elimination, controls, treatment assignment and randomization, precision, replication, power and sample size calculations, stratification, and ethics. Suitable for graduate students in biostatistics and for research-oriented graduate students in other scientific fields. Prerequisite: BIOST 511 or equivalent, and one of BIOST 513, BIOST 518, STAT 421, STAT 423, STAT 512, or EPI 512; or permission of instructor. Offered: jointly with STAT 524; Sp.

BIOST 525 Advanced Methods for Global Health III (4)
Focuses on applying advanced non-randomized methods to quantitatively evaluate global health implementation science questions, including a specific focus on applying difference-in-differences, interrupted time-series, and regression discontinuity designs. Assumes prior knowledge of generalized linear models and modern methods to analyze correlated data, including generalized estimating equations (GEE) and random-effects models. Prerequisite: either BIOST 540, CS&SS 560/SOC 560/STAT 560, or permission of instructor; recommended: EPI 512 and EPI 513. Offered: jointly with EPI 556/G H 537/HMS 537; Sp.

BIOST 526 Bayesian Biostatistics (3)
Introduction to Bayesian methods for data analysis; Bayesian reasoning, prior elicitation, inference and decision making, and computation applied to biomedical research. Prerequisite: any course in statistics at the 400-level or higher or instructor's permission. Offered: jointly with EPI 540/HEOR 550; Sp.

BIOST 527 Nonparametric Regression and Classification (3)
Covers techniques for smoothing and classification including spline models, kernel methods, generalized additive models, and the averaging of multiple models. Describes measures of predictive performance, along with methods for balancing bias and variance. Prerequisite: either STAT 502 and STAT 504 or BIOST 514 and BIOST 515. Offered: jointly with STAT 527; Sp.

BIOST 528 Advanced Methods for Global Health II (4)
Presents applications of the cluster-randomized trial design to estimate the impact of interventions for a global health and implementation science audience. Covers trial design and implementation, reviews methods commonly used for analysis. Assumes prior knowledge of generalized linear models and modern methods to analyze correlated data, including generalized estimating equations (GEE) and random-effects models. Prerequisite: either BIOST 540, CS&SS 560/SOC 560/STAT 560, or permission of instructor; recommended: EPI 512 and EPI 513. Offered: jointly with EPI 553/G H 536/HMS 536; W.

BIOST 529 Sample Survey Techniques (3)
Design and implementation of selection and estimation procedures. Emphasis on human populations. Simple, stratified, and cluster sampling; multistage and two-phase procedures; optimal allocation of resources; estimation theory; replicated designs; variance estimation; national samples and census materials. Prerequisite: either STAT 421, STAT 423, STAT 504, QMETH 500, BIOST 511, or BIOST 517, or equivalent; or permission of instructor. Offered: jointly with CS&SS 529/STAT 529.

BIOST 531 Statistical Methods for Analysis with Missing Data (3)
Covers statistical methods for the analysis of missing data, including likelihood-based, weighted GEE, multiple imputation, and Bayesian approaches. Uses computational tools such as EM algorithm and Gibbs' sampler. Covers both ignorable and non-ignorable missing-data mechanisms as well as cross-sectional and longitudinal study designs. Primarily uses data arising from epidemiologic studies. Offered: jointly with EPI 531.

BIOST 532 Research Integrity in the Data Sciences (2)
Considers research integrity in the conduct of biomedical research, particularly regarding analysis of data and the interpretation and communication of statistics. Provides knowledge and resources to facilitate upholding the highest standards of research integrity. Helps students formulate justified responses to challenges, and nurtures a sense of professional responsibility to take action. Credit/no-credit only. Offered: jointly with ENV H 537; Sp.

BIOST 533 Theory of Linear Models (3)
Examines model structure; least squares estimation; Gauss-Markov theorem; central limit theorems for linear regression; weighted and generalized least squares; fixed and random effects; analysis of variance; blocking and stratification; and applications in experimental design. Prerequisite: STAT 421 or STAT 423 or BIOST 515; and STAT 513; and a course in matrix algebra. Offered: jointly with STAT 533; Sp.

BIOST 534 Statistical Computing (3)
Introduction to scientific computing. Includes programming tools, modern programming methodologies, (modularization, object oriented design), design of data structures and algorithms, numerical computing and graphics. Uses C++ for several substantial scientific programming projects. Prerequisite: experience with programming in a high level language. Offered: jointly with STAT 534; Sp.

BIOST 536 Categorical Data Analysis in Epidemiology (4)
Summary of univariate categorical data analysis; introduction to multivariate analysis of categorical epidemiologic and health sciences data using multiplicative models. Experience at interpretation; familiarity with available software programs gained by analysis of bona fide data and critiques of published analyses appearing in literature. Prerequisite: BIOST 515; EPI 513 and either BIOST 513 or BIOST 518; or permission of instructor. Offered: jointly with EPI 536; A.

BIOST 537 Survival Data Analysis in Epidemiology (4)
Univariate and multivariate analysis of right-censored survival data. Kaplan-Meier estimation of survival curves; proportional hazards regression; accelerated failure time models; parametric modeling of survival data; model diagnostics; time-varying covariates; delayed entry. Prerequisite: either BIOST 513, BIOST 515, BIOST 518, or permission of instructor. Offered: jointly with EPI 537; W.

BIOST 540 Longitudinal and Multilevel Data Analysis (3)
Introduction to regression modeling of longitudinal and clustered data from epidemiology and health sciences. Interpretation and familiarity with software gained by analysis of data and critiques of published analyses. Course overlaps with: CS&SS 592/SOC WL 592. Prerequisite: either BIOST 513, BIOST 515, BIOST 518, BIOST 536, or permission of instructor. Offered: Sp.

BIOST 544 Introduction to Biomedical Data Science (3/4)
Provides an introduction to biomedical data science with an emphasis on statistical perspectives, inducing the process of collecting, organizing, and integrating information toward extracting knowledge from data in public health, biology, and medicine. Prerequisite: either BIOST 511 or equivalent; either BIOST 509 or equivalent; or permission of instructor. Offered: A.

BIOST 545 Biostatistical Methods for Big Omics Data (3)
This "hands-on" course introduces statistical methods for high-dimensional omics data, as well as the R programming language and the Bioconductor project as tools to extract, query, integrate, visualize, and analyze real world omics data sets. Prerequisite: BIOST 512, 514, or 517. Offered: jointly with GENOME 545/PHG 545.

BIOST 546 Machine Learning for Biomedical and Public Health Big Data (3)
Provides an introduction to statistical learning for biomedical and public health data. Intended for graduate students in SPH/SOM. Prerequisite: one of the following: (1) BIOST 511 and BIOST 512 (which may be taken concurrently); (2) BIOST 514 and BIOST 515 (which may be taken concurrently); or (3) BIOST 517 and BIOST 518 (which may be taken concurrently). Offered: W.

BIOST 550 Statistical Genetics I: Mendelian Traits (3)
Mendelian genetic traits. Population genetics; Hardy-Weinberg, allelic variation, subdivision. Likelihood inference, information and power; latent variables and EM algorithm. Pedigree relationships and gene identity. Meiosis and recombination. Linkage detection. Multipoint linkage analysis. Prerequisite: STAT 390 and STAT 394, or permission of instructor. Offered: jointly with STAT 550; Sp.

BIOST 551 Statistical Genetics II: Quantitative Traits (3)
Statistical basis for describing variation in quantitative traits. Decomposition of trait variation into components representing genes, environment and gene-environment interaction. Methods of mapping and characterizing quantitative trait loci. Prerequisite: STAT/BIOST 550; STAT 423 or BIOST 515; or permission of instructor. Offered: jointly with STAT 551; A.

BIOST 552 Statistical Genetics III: Design and Analysis (3)
Overview of probability models, inheritance models, penetrance. Association and linkage. The lod score method. Affected sib method. Fitting complex inheritance models. Design mapping studies; multipoint, disequilibrium, and fine-scale mapping. Ascertainment. Prerequisite: STAT/BIOST 551; GENOME 371; or permission of instructor. Offered: jointly with STAT 552; W.

BIOST 555 Statistical Methods for Spatial Epidemiology (3)
Motivates the need for, and describes methods for the analysis of spatially indexed epidemiological data. Covers four major topics: clustering and cluster detection, disease mapping, spatial regression, and an introduction to geographical information systems. Considers both point-references and spatially aggregated data. Course overlaps with: CS&SS 554/SOC 534/STAT 554. Prerequisite: either BIOST 513, BIOST 518, BIOST 522, SOC 506/CS&SS 507, or STAT 512. Offered: jointly with EPI 555/G H 534.

BIOST 556 Introduction to Statistics and Probability (5)
Overview of probability; conditional probability and independence; Bayes Theorem; discrete and continuous random variables including jointly distributed; key distributions including the normal and its spin offs; properties of expectation and variance; conditional expectation; covariance and correlation; Central Limit Theorem; law of large numbers; Parameter Estimation. Offered: jointly with DATA 556/STAT 556; A.

BIOST 557 Applied Statistics and Experimental Design (5)
Inferential statistical methods for discrete and continuous random variables including tests for difference in means and proportions; linear and logistic regression; causation versus correlation; confounding; resampling methods; study design. Prerequisite: either STAT 556/BIOST 556/DATA 556 or permission of instructor. Offered: jointly with DATA 557/STAT 557; W.

BIOST 558 Statistical Machine Learning for Data Scientists (5)
Bias-variance trade-off; training versus test error; overfitting; cross-validation; subset selection methods; regularized approaches for linear/logistic regression: ridge and lasso; non-parametric regression: trees, bagging, random forests; local regression and splines; generalized additive models; support vector machines; k-means and hierarchical clustering; principal components analysis. Prerequisite: STAT/BIOST/DATA 557, or permission of instructor. Offered: jointly with DATA 558/STAT 558; Sp.

BIOST 561 Computational Skills for Biostatistics I (2)
Provides an introduction to statistical computing with R. Emphasizes good programming techniques useful in statistical analysis. Prerequisite: biostatistics graduate student. Credit/no-credit only. Offered: Sp.

BIOST 562 Computational Skills for Biostatistics II (1)
Provides an introduction to statistical computing with R. Emphasizes good programming techniques useful in statistical analysis. Prerequisite: either BIOST 561 or permission of instructor. Credit/no-credit only.

BIOST 563 Computing and Research (2)
Provides an introduction to statistical computing with R. Emphasizes good programming techniques useful in statistical analysis. Prerequisite: either BIOST 562 or permission of instructor. Credit/no-credit only.

BIOST 565 Statistical Evaluation of Biomarkers (3)
Covers evaluation of biomarkers for diagnosis; decision-theoretic assessment tools; measures of the incremental value of a new biomarker; evaluation of risk prediction models; evaluation of biomarkers for prognosis or for guiding therapy; statistical learning for developing a diagnostic/prognostic score; and clinical trial designs for biomarker guided decisions. Prerequisite: BIOST 515; either BIOST 518 or both BIOST 512 and BIOST 513; or equivalent.

BIOST 570 Advanced Regression Methods for Independent Data (4)
Covers linear models, generalized linear and non-linear regression, and models. Includes interpretation of parameters, including collapsibility and non-collapsibility, estimating equations; likelihood; sandwich estimations; the bootstrap; Bayesian inference: prior specification, hypothesis testing, and computation; comparison of approaches; and diagnostics. Prerequisite: either STAT 512 and STAT 513, or BIOST 522 and BIOST 523; and either STAT 502 and STAT 504/CS&SS 504, or BIOST 514 and BIOST 515; recommended: matrix algebra from a course at the level of BIOST 533/STAT 533. Offered: jointly with STAT 570; A.

BIOST 571 Advanced Regression Methods for Dependent Data (4)
Covers longitudinal data models, generalized linear and non-linear mixed models; marginal versus conditional models; generalized estimating equations, likelihood-based inference, REML, BLUP, and computation of integrals; Bayesian inference: Markov chain Monte Carlo; covariance models, including models for split plot designs; comparison of approaches; and diagnostics. Prerequisite: BIOST570/STAT 570. Offered: jointly with STAT 571; W.

BIOST 572 Preparation for Research Prelim (3)
Student presentations and discussion on selected methodological research articles focusing on regression modeling. Prerequisite: BIOST 571/STAT 571. Offered: jointly with STAT 572; Sp.

BIOST 576 Statistical Methods for Survival Data (3)
Statistical methods for censored survival data arising from follow-up studies on human or animal populations. Parametric and nonparametric methods, Kaplan-Meier survival curve estimator, comparison of survival curves, log-rank test, regression models including the Cox proportional hazards model, competing risks. Prerequisite: STAT 581 and either BIOST 515, STAT 473, or equivalent. Offered: jointly with STAT 576.

BIOST 578 Special Topics in Advanced Biostatistics (*, max. 30)
Advanced-level topics in biostatistics offered by regular and visiting faculty. Prerequisite: permission of instructor. Offered: jointly with STAT 578; AWSpS.

BIOST 579 Data Analysis and Reporting (2/3, max. 12)
Analysis of real data to answer scientific questions. Common data-analytic problems. Sensible approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients. Graduate standing in statistics or biostatistics. Credit/no-credit only. Offered: jointly with STAT 579; SpS.

BIOST 580 Seminar in Biostatistics (*, max. 30)
Presentation and discussion of special topics and research results in biostatistics. Speakers include resident faculty, visiting scientists, and advanced graduate students. Offered: AWSp.

BIOST 581 Statistical Genetics Seminar (1, max. 30)
Presentations and discussion of special topics and research results in statistical genetics. Students, posdocs, and faculty present their work and papers from the literature. Credit/no-credit only. Offered: AWSp.

BIOST 582 Student Seminar (1, max. 30)
Student seminar series for collaboration, exchange of ideas, and exposure to different stages of performing independent research. Encourages both students and faculty to give presentations including RA work, extended class projects, master's theses, dissertation progress, data analysis, practice talks, and journal articles. Credit/no-credit only. Offered: AWSp.

BIOST 583 Advanced Theory of Statistical Inference I (4)
Foundations of parametric statistics: elementary decision theory, Bayesian methods, modes of convergence, central limit theorems, delta method, maximum likelihood estimation, regularity, hypothesis testing under fixed and local alternatives, parametric efficiency theory. Prerequisite: STAT 513. ; recommended: mathematical analysis from a course at the level of either MATH 426 or STAT 559. Offered: jointly with STAT 581; A.

BIOST 584 Advanced Theory of Statistical Inference II (4)
Semiparametric and nonparametric estimation of irregular parameters: minimax rates of convergence, kernel methods, bias-variance tradeoff, concentration inequalities, empirical risk minimization, Rademacher complexity, Vapnik-Chervonenkis dimension, covering and bracketing numbers, empirical process theory (Glivenko-Cantelli results). Prerequisite: STAT 581/BIOST 583. ; recommended: mathematical analysis from a course at the level of either MATH 426 or STAT 559. Offered: jointly with STAT 582; W.

BIOST 585 Advanced Theory of Statistical Inference III (4)
Semiparametric and nonparametric estimation of regular parameters: weak convergence, empirical process theory (Donsker results), asymptotic linearity, estimating equations, U-statistics, functional delta method, efficiency theory, construction of efficient estimators. Prerequisite: STAT 582/BIOST 584. ; recommended: mathematical analysis from a course at the level of either MATH 426 or STAT 559. Offered: jointly with STAT 583; Sp.

BIOST 588 Special Topics in Biostatistical Practice (1-10, max. 30)
Selected topics in biostatistical practice.

BIOST 590 Biostatistical Consulting (3, max. 6)
Training in consulting on the biostatistical aspect of research problems arising in the biomedical field. Students, under the supervision of a faculty member, participate in discussions with investigators leading to the design and/or the analysis of a quantitative investigation of a problem. With experience, independent associations of student and research worker are encouraged, with subsequent review by faculty of resulting design and analysis. Prerequisite: permission of instructor. Offered: AWSpS.

BIOST 591 Applied Research Project ([1-3]-, max. 3)
Project-based course. Fulfills applied requirement of Biostatistics PhD program. Prerequisite: permission of the Applied Requirement Committee (ARC). Credit/no-credit only. Offered: AWSpS.

BIOST 595 Biostatistics Master's Practicum (1-12, max. 12)
Supervised practice experience providing students an opportunity to learn how biostatistics is applied in a public health setting and in the formation of public health policy. Prerequisite: BIOST 514; BIOST 515; BIOST 536; BIOST 537.

BIOST 596 Biostatistics Capstone I - Project Planning (3)
Project sponsors introduce students to health data analytics challenges. Students form collaborative teams, each of which writes, presents and revises a project proposal that outlines the approach and methods the group plans to use. Prerequisite: BIOST 504; BIOST 514; BIOST 515; BIOST 522; BIOST 523; BIOST 561; and BIOST 579, or permission of instructor. Credit/no-credit only. Offered: A.

BIOST 597 Biostatistics Capstone II - Project Implementation (3)
Student teams implement their project proposals. At the end of the course, teams share their results in oral and written form, and prepare materials for individual portfolios. Prerequisite: BIOST 596, or permission of instructor. Credit/no-credit only. Offered: W.

BIOST 598 Techniques of Statistical Consulting (1)
Seminar series covering technical and non-technical aspects of statistical consulting, including skills for effective communication with clients, report writing, statistical tips and rules of thumb, issues in survey sampling, and issues in working as a statistical consultant in academic, industrial, and private-practice settings. Prerequisite: entry code. Offered: jointly with STAT 598; ASp.

BIOST 600 Independent Study or Research (*-)
Offered: AWSpS.

BIOST 700 Master's Thesis (*-)
Offered: AWSpS.

BIOST 800 Doctoral Dissertation (*-)
Offered: AWSpS.