AndeeKaplan

Ph.D. candidate at Iowa State University Statistics

Coursework at Iowa State

    Stat 500. Statistical Methods. Introduction to methods for analyzing data from experiments and observational data. Design-based and model-based inference. Estimation, hypothesis testing, and model assessment for 2 group and k group studies. Experimental design and the use of pairing/blocking. Analysis of discrete data. Correlation and regression, prediction, model selection and diagnostics. Simple mixed models including nested random effects and split plot experimental designs. Use of the SAS statistical software.

    Stat 503. Exploratory Methods and Data Mining. Approaches to finding the unexpected in data; exploratory data analysis; pattern recognition; dimension reduction; supervised and unsupervised classification; interactive and dynamic graphical methods; computer-intensive statistical techniques for large or high dimensional data and visual inference. Emphasis is on problem solving, topical problems, and learning how so-called black-box methods actually work.

    Stat 511. Statistical Methods. Introduction to the general theory of linear models, least squares and maximum likelihood estimation, hypothesis testing, interval estimation and prediction, analysis of unbalanced designs. Models with both fixed and random factors. Introduction to non-linear and generalized linear models, bootstrap estimation, smoothing methods. Requires use of R statistical software.

    Stat 520. Statistical Methods III. Nonlinear regression; generalized least squares; asymptotic inference. Generalized linear models; exponential dispersion families; maximum likelihood and inference. Designing Monte Carlo studies; bootstrap; cross-validation. Fundamentals of Bayesian analysis; data models, priors and posteriors; posterior prediction; credible intervals; Bayes Factors; types of priors; simulation of posteriors; introduction to hierarchical models and Markov Chain Monte Carlo methods.

    Stat 542. Theory of Probability and Statistics I. Sample spaces, probability, conditional probability; Random variables, univariate distributions, expectation, median, and other characteristics of distributions, moment generating functions; Joint distributions, conditional distributions and independence, correlation and covariance; Probability laws and transformations; Introduction to the Multivariate Normal distribution; Sampling distributions, order statistics; Convergence concepts, the law of large numbers, the central limit theorem and delta method; Basics of stochastic simulation.

    Stat 543. Theory of Probability and Statistics II. Point estimation including method of moments, maximum likelihood estimation, exponential family, Bayes estimators, Loss function and Bayesian optimality, unbiasedness, sufficiency, completeness, Basu's theorem; Interval estimation including confidence intervals, prediction intervals, Bayesian interval estimation; Hypothesis testing including Neyman-Pearson Lemma, uniformly most powerful tests, likelihood ratio tests; Bayesian tests; Large sample properties of maximum likelihood estimators and likelihood ratio tests; Nonparametric methods, bootstrap.

    Stat 544. Bayesian Statistics. Specification of probability models; subjective, conjugate, and noninformative prior distributions; hierarchical models; analytical and computational techniques for obtaining posterior distributions; model checking, model selection, diagnostics; comparison of Bayesian and traditional methods.

    Stat 551. Times Series Analysis. Concepts of trend and dependence in time series data; stationarity and basic model structures for dealing with temporal dependence; moving average and autoregressive error structures; analysis in the time domain and the frequency domain; parameter estimation, prediction and forecasting; identification of appropriate model structure for actual data and model assessment techniques. Possible extended topics include dynamic models and linear filters.

    Stat 579. An Introduction to R. An introduction to the logic of programming, numerical algorithms, and graphics. The R statistical programming environment will be used to demonstrate how data can be stored, manipulated, plotted, and analyzed using both built-in functions and user extensions. Concepts of modularization, looping, vectorization, conditional execution, and function construction will be emphasized.

    Stat 580. Statistical Computing. Introduction to scientific computing for statistics using tools and concepts in R: programming tools, modern programming methodologies, modularization, design of statistical algorithms. Introduction to C programming for efficiency; interfacing R with C. Building statistical libraries. Use of algorithms in modern subroutine packages, optimization and integration. Implementation of simulation methods; inversion of probability integral transform, rejection sampling, importance sampling. Monte Carlo integration.

    Stat 585X. Data Technologies for Statistical Analysis. Introduction to computational methods for data analysis. Accessing and managing data formats: flat files, databases, web technologies based on mark-up languages (SML, KML, HTML), netCDF. Elements of text processing: regular expressions for cleaning data. Working with massive data, handling missing data, scaled computing. Efficient programming, reproducible code.

    Stat 601. Advanced Methods. Methods of constructing complex models including adding parameters to existing structures, incorporating stochastic processes and latent variables. Use of modified likelihood functions; quasi-likelihoods; profiles; composite,likelihoods. Asymptotic normality as a basis of inference; Godambe information. Sample reuse; block bootstrap; resampling with dependence. Simulation for model assessment. Issues in Bayesian analysis.

    Stat 602. Modern Multivariate Statistical Learning. Statistical theory and methods for modern data mining and machine learning, inference, and prediction. Variance-bias trade-offs and choice of predictors; linear methods of prediction; basis expansions; smoothing, regularization, and reproducing kernel Hilbert spaces; kernel smoothing methods; neural networks and radial basis function networks; bootstrapping, model averaging, and stacking; linear and quadratic methods of classification; support vector machines; trees and random forests; boosting; prototype methods; unsupervised learning including clustering, principal components, and multi-dimensional scaling; kernel mechanics.

    Stat 641. Foundations of Probability Theory. Sequences and set theory; Lebesgue measure, measurable functions. Absolute continuity of functions, integrability and the fundamental theorem of Lebesgue integration. General measure spaces, probability measure, extension theorem and construction of Lebesgue-Stieljes measures on Euclidean spaces. Measurable transformations and random variables, induced measures and probability distributions. General integration and expectation, Lp-spaces and integral inequalities. Uniform integrability and absolute,continuity of measures. Probability densities and the Radon-Nikodym theorem. Product spaces and Fubini-Tonelli theorems.

    Stat 642. Advanced Theory of Statistical Inference. Probability spaces and random variables. Kolmogorov's consistency theorem. Independence, Borel-Cantelli lemmas and Kolmogorov's 0 - 1 Law. Comparing types of convergence for random variables. Sums of independent random variables, empirical distributions, weak and strong laws of large numbers. Convergence in distribution and its characterizations, tightness, characteristic functions, central limit theorems and Lindeberg-Feller conditions. Conditional probability and expectation. Discrete parameter martingales and their properties and applications.

    Stat 643. Advanced Theory of Statistical Inference. Sufficiency and related concepts, completeness, exponential families and statistical information. Elements of decision theory, decision rules, invariance and Bayes rule. Maximum likelihood and asymptotic inference. Generalized estimating equations and estimating functions, M-estimation, U-statistics. Likelihood ratio tests, simple and composite hypotheses, multiple testing. Bayesian inference. Nonparametric inference, bootstrap, empirical likelihood, and tests for nonparametric models.

    Stat 651. Advanced Time Series. Stationary and nonstationary time series models, including ARMA, ARCH, and GARCH. Covariance and spectral representation of time series. Fourier and periodogram analyses. Predictions. CLT for mixing processes. Estimation and distribution theory. Long range dependence.

    Math 501. Introduction to Real Analysis. A development of the real numbers. Study of metric spaces, completeness, compactness, sequences, and continuity of functions. Differentiation and integration of real-valued functions, sequences of functions, limits and convergence, equicontinuity.