UC Davis 2008-2010 General Catalog - General Catalog - UC Davis

Statistics 489
137. Applied Time Series Analysis (4)
Lecture—3 hours; term paper. Prerequisite: course
108 or the equivalent. Time series relationships,
cyclical behavior, periodicity, spectral analysis,
coherence, filtering, regression, ARIMA and statespace
models; Applications to data from economics,
engineering, medicine environment using time series
software.—III. (III.)
138. Analysis of Categorical Data (4)
Lecture—3 hours; discussion—1 hour. Prerequisite:
course 130B or 131B, or courses 106 and 108.
Varieties of categorical data, cross-classifications,
contingency tables, tests for independence. Multidimensional
tables and log-linear models, maximum
likelihood estimation; tests of goodness-of-fit. Logit
models, linear logistic models. Analysis of incomplete
tables. Packaged computer programs, analysis
of real data. GE credit: SciEng.—I. (I.)
141. Statistical Computing (4)
Lecture—3 hours; laboratory—1 hour. Prerequisite:
course 130A or 131A, and one of courses 13, 32,
100, 102, or the equivalent, and experience in computer
programming; course 130B or 131B recommended.
Use of computers in statistics. Numerical
foundations of statistical procedures. Computation of
probabilities and quantiles. Random numbers.
Monte Carlo method and bootstrap. Methods for
parametric statistical models. Graphical methods
and exploratory data analysis.—II. (II.)
142. Reliability (4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course 130B or 131B or consent of
instructor. Stochastic modeling and inference for reliability
systems. Topics include coherent systems, statistical
failure models, notions of aging, maintenance
policies and their optimization. Offered in alternate
years.
144. Sampling Theory of Surveys (4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course 130B or 131B. Simple random,
stratified random, cluster, and systematic sampling
plans; mean, proportion, total, ratio, and regression
estimators for these plans; sample survey design,
absolute and relative error, sample size selection,
strata construction; sampling and nonsampling
sources of error. Offered in alternate years. GE
credit: SciEng.—(I.)
145. Bayesian Statistical Inference (4)
Lecture—3 hours; laboratory—1 hour. Prerequisite:
courses 130A and 130B, or 131A and 131B, or the
equivalent. Subjective probability, Bayes Theorem,
conjugate priors, non-informative priors, estimation,
testing, prediction, empirical Bayes methods, properties
of Bayesian procedures, comparisons with classical
procedures, approximation techniques, Gibbs
sampling, hierarchical Bayesian analysis, applications,
computer implemented data analysis. Offered
in alternate years.—(II.)
190X. Seminar (1-2)
Seminar—1-2 hours. Prerequisite: one of courses
13, 32, 100, 102, or 103. In-depth examination of
a special topic in a small group setting.
192. Internship in Statistics (1-12)
Internship—3-36 hours; term paper. Prerequisite:
upper division standing and consent of instructor.
Work experience in statistics. (P/NP grading only.)
194HA-194HB. Special Studies for Honors
Students (4-4)
Independent study—12 hours. Prerequisite: senior
qualifying for honors. Directed reading, research
and writing, culminating in the completion of a
senior honors thesis or project under direction of a
faculty adviser. (Deferred grading only, pending
completion of sequence.)
198. Directed Group Study (1-5)
Prerequisite: consent of instructor. (P/NP grading
only.)
199. Special Study for Advanced
Undergraduates (1-5)
Prerequisite: consent of instructor. (P/NP grading
only.)
Graduate Courses
205. Statistical Methods for Research (4)
Lecture—3 hours; laboratory—1 hour. Prerequisite:
course 106 or the equivalent. Topics in design of
experiments include factorial designs, balanced and
unbalanced experiments, random and mixed effects
models, response surface methodology, nested
design, repeated measures, cross-over design, analysis
of covariance. Applications in engineering, biological
sciences, medicine and environmental
research. Offered in alternate years.—(III.)
222. Biostatistics: Survival Analysis (4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course 131C. Incomplete data; life
tables; nonparametric methods; parametric methods;
accelerated failure time models; proportional hazards
models; partial likelihood; advanced topics.
(Same course as Biostatistics 222.)—I. (I.)
223. Biostatistics: Generalized Linear
Models (4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course 131C. Likelihood and linear
regression; generalized linear model; Binomial
regression; case-control studies; dose-response and
bioassay; Poisson regression; Gamma regression;
quasi-likelihood models; estimating equations; multivariate
GLMs. (Same course as Biostatistics 223.)—
II. (II.)
224. Analysis of Longitudinal Data (4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course/Biostatistics 222, 223 and
course 232B or consent of instructor. Standard and
advanced methodology, theory, algorithms, and
applications relevant for analysis of repeated measurements
and longitudinal data in biostatistical and
statistical settings. (Same course as Biostatistics
224.)—III. (III.)
225. Clinical Trials (4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course/Biosatistics 223 or consent of
instructor. Basic statistical principles of clinical
designs, including bias, randomization, blocking,
and masking. Practical applications of widely-used
designs, including dose-finding, comparative and
cluster randomization designs. Advanced statistical
procedures for analysis of data collected in clinical
trials. (Same course as Biostatistics 225.) Offered in
alternate years.—III.
226. Statistical Methods for Bioinformatics
(4)
Lecture—3 hours; discussion/laboratory—1 hour.
Prerequisite: course 131C or consent of instructor;
data analysis experience recommended. Standard
and advanced statistical methodology, theory, algorithms,
and applications relevant to the analysis of -
omics data. (Same course as Biostatistics 226.)
Offered in alternate years.—(II.)
231A. Mathematical Statistics I (4)
Lecture—3 hours; discussion—1 hour. Prerequisite:
course 131A, 131B, 131C, Mathematics 127A,
127B or the equivalent. First part of three-quarter
sequence on mathematical statistics. Emphasizes
foundations. Topics include basic concepts in asymptotic
theory, decision theory (e.g. risk function, Bayes
and minimax optimality, Bayes estimation), and an
overview of methods of point estimation.—I. (I.)
231B. Mathematical Statistics II (4)
Lecture—3 hours; discussion—1 hour. Prerequisite:
course 231A. Second part of a three-quarter
sequence on mathematical statistics. Emphasizes
large sample theory, e.g. asymptotics of MLE, likelihood-ratio-test
and Chi-square-test, CLT with applications
in (generalized) linear models. Classical
hypothesis testing, e.g. Neyman-Pearson theory,
UMP (unbiased)-tests.—II. (II.)
231C. Mathematical Statistics III (4)
Lecture—3 hours; discussion—1 hour. Prerequisite:
course 231A, 231B. Third part of three-quarter
sequence on mathematical statistics. Emphasizes
large sample theory and their applications. Topics
include statistical functionals (applications to L- and
M-estimation); resampling methods (jackknife, bootstrap);
curve estimation (density, regression, failure
rate); rank tests, and one instructor-selected topic.—
III. (III.)
232A. Applied Statistics I (4)
Lecture—3 hours; laboratory—1 hour. Prerequisite:
course 106, 108, 131A, 131B, 131C, Mathematics
167. Estimation and testing for the general linear
model, ANOVA design, model validation, variable
selection, and analyzing data with the linear
model.—I. (I.)
232B. Applied Statistics II (4)
Lecture—3 hours; laboratory—1 hour. Prerequisite:
course 232A. Estimation and testing for the general
linear mixed model, Bayesian hierarchical modeling,
nonparametric modeling, analyzing data and
designing experiments with respect to these models.—II.
(II.)
232C. Applied Statistics III (4)
Lecture—3 hours; laboratory—1 hour. Prerequisite:
course 232B. Multivariate analysis: multivariate distributions,
multivariate linear models, data analytic
methods including principal component, factor, discriminant,
cluster, and canonical correlation analyses,
nonparametric methods, regression trees, and
Bayesian methods.—III. (III.)
233. Design of Experiments (3)
Lecture—3 hours. Prerequisite: course 131C. Topics
from balanced and partially balanced incomplete
block designs, fractional factorials, and response
surfaces. Offered in alternate years.—(III.)
235A-235B-235C. Probability Theory (4-4-
4)
Lecture—3 hours; term paper or discussion—1 hour.
Prerequisite: 235A—Mathematics 125B and 135A
or course 131A or consent of instructor; 235B—
Mathematics 235A/course 235A or consent of
instructor; 235C—Mathematics 235B/course 235B
or consent of instructor. Measure-theoretic foundations,
abstract integration, independence, laws of
large numbers, characteristic functions, central limit
theorems. Weak convergence in metric spaces,
Brownian motion, invariance principle. Conditional
expectation. Topics selected from martingales,
Markov chains, ergodic theory. (Same course as
Mathematics 235A-235B-235C.)—I-II-III. (I-II-III.)
237A-237B. Time Series Analysis (4-4)
Lecture—3 hours; term paper. Prerequisite: course
131B or the equivalent; course 237A is a prerequisite
for course 237B. Advanced topics in time series
analysis and applications. Models for experimental
data, measures of dependence, large-sample theory,
statistical estimation and inference. Univariate and
multivariate spectral analysis, regression, ARIMA
models, state-space models, Kalman filtering.
Offered in alternate years.—(I-II.)
238. Theory of Multivariate Analysis (4)
Lecture—3 hours; term paper. Prerequisite: courses
131B and 135. Multivariate normal and Wishart
distributions, Hotelling’s T-Squared, simultaneous
inference, likelihood ratio and union intersection
tests, Bayesian methods, discriminant analysis, principal
component and factor analysis, multivariate
clustering, multivariate regression and analysis of
variance, application to data. Offered in alternate
years.—II.
240A-240B. Nonparametric Inference (4-4)
Lecture—3 hours; term paper. Prerequisite: course
231C; courses 235A-235B-235C recommended.
Comprehensive treatment of nonparametric statistical
inference, including the most basic materials
from classical nonparametrics, robustness, nonparametric
estimation of a distribution function from
incomplete data, curve estimation, and theory of
resampling methodology. Offered in alternate years.
(II-III.)
241. Asymptotic Theory of Statistics (4)
Lecture—3 hours; term paper. Prerequisite: course
231C; courses 235A-235B-235C desirable. Topics
in asymptotic theory of statistics chosen from weak
convergence, contiguity, empirical processes, Edgeworth
expansion, and semiparametric inference.
Offered in alternate years. (III.)
Quarter Offered: I=Fall, II=Winter, III=Spring, IV=Summer; 2009-2010 offering in parentheses
General Education (GE) credit: ArtHum=Arts and Humanities; SciEng=Science and Engineering; SocSci=Social Sciences; Div=Social-Cultural Diversity; Wrt=Writing Experience