Course Content

Understand the fundamentals of statistics and data science, understanding the difference between a population and a sample in a given dataset. Basic statistical definitions and learn how to work with different sorts of data. How to visualize different data types. Descriptive statistics: measures of central tendency, asymmetry, variability, correlation and covariance. What are the random variables in a dataset? Distinguish and work with different distributions that describe distinct data sources.

Course Content

Basic statistical analyses in different types of data. Sampling distributions of distinct data sources. Inferential statistics in the data science. Estimation, confidence intervals and hypothesis testing under various data types. Distribution fitting and analysis of variance for one factor design in a given dataset. Linear regression and association between two categoric variables. Basic nonparametric procedures under various data types.

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Basic definitions and managing different types of data. Introduction to manipulation (indexing, subsetting, reshaping, transforming etc.), visualization, mapping and analysis of data. Dealing with common problems like missing or inconsistent values in datasets. Use of related R and/or Python programming packages. Merging multiple data tables (equivalent to an SQL JOIN)

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Experiments and events. Set theory. Axioms and basic theorems of probability. Finite sample spaces and counting techniques. Independent events. Conditional probability. Random variables and distributions. Expectation, variance, covariance and correlation. Some special distributions.

Course Content

Random samples. Sample mean and variance. Chebychev`s inequality. Law of large numbers. Central limit theorem. Estimation. Maximum likelihood, unbiased, minimum variance unbiased, consistent and efficient estimators. Sufficiency. Confidence intervals. Hypothesis testing. Introduction to nonparametric methods. Regression and analysis of variance.

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Sample space, events, basic combinatorial probability, conditional probability, Bayes theorem, independence, random variables, distributions, expectation.

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Transformations of random variables, generating functions, conditional expectation, limit theorems, central limet theorem, limiting distributions.

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Introduction to probability. Finite sample spaces. Conditional probability and independence. One dimensional random variables. Functions of random variables. Further characterization of random variables. Discrete random variables. Continuous random variables. Random sample sample and statistics. Statistical inference, estimation and tests of hypotheses.

Course Content

Sampling distributions. Sample drawing techniques. Estimation and testing for one or two population characteristics. Maximum likelihood estimation of parameters. Measures of association. Simple and multiple regression. Introduction to design of experiments, analysis of variance; one-way, multiway classifications. Multiple comparisons. Basic nonparametric procedures. Elementary time series analysis; trends, seasonality, forecasting. Indexing. Some applications in medicine, science, engineering and social sciences.

Course Content

Accuracy in numerical computations. Linear simultaneous algebraic equations. Eigen-values and eigen-vectors. Nonlinear equations. Interpolation. Finite differences. Numerical approximation. Numerical solution of unconstrained and constrained optimization. Numerical differentiation. Numerical integration. Numerical solution of integral value problem.

Course Content

Introduction to statistical techniques in statistical software available in the department or on the campus. Managing and analyzing data using statistical database packages like R. Introduction to MATLAB with applications to matrix algebra.

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Introduction to programming and computation. Introduction to computer organization and basic data structures. An advanced programming language with applications to statistical procedures.
Prerequisite: CENG 230

Course Content

Common theoretical distributions. Sampling distributions. Principles of point estimation. Techniques of estimation. Properties of point estimators. Optimality criteria in estimation. Selected topics from robust inference. Bayesian inference.

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Region (interval) estimation. Hypathesis testing. Optimality properties for hypothesis testing. Likelihood ratio tests. Sequential tests.

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Introduction to database systems. Relational databases. Entity relationship (ER) model. Normalization. Structured Query Language (SQL). Designing databases. Introduction to distributed, parallel and object databases. Big data storage systems. Datawarehouses. Online Analytic Processing (OLAP). Big data analytics and NoSQL. Web data management. Cloud computing.

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Types of data. Graphical and tabular represantation of data. Approaches to finding the unexpected in data. Exploratory data analysis for large and high-dimensional data. Analysis of categorical data. Elements of robust estimation. Handling missing data. Smoothing methods. Data mining.
Prerequisites: STAT 156, STAT 291

Course Content

distributions. Monte Carlo methods for inferential statistics. Resampling. Data partitioning. Cross-validation. Bootstraping. Jackknifing. Tools for exploratory and graphical data analysis. Nonparametric probability density estimation.
Prerequisite: STAT 291

Course Content

Simple and Multiple Linear Regression Models. Estimation, interval estimation and test of hypothesis on the parameters of the models. Model Adequecy Checking. Multicollinearity. Transformation.
Prerequisites: MATH 260, STAT 156

Course Content

Simple Nonlinear Models, Less than Full Rank Models: One-way,Two-way Models and Multiple Comparison Tests, Analysis of Covariance (ANCOVA) Model, Introduction to Generalized Linear Models (GLM), Poisson Regression, Logistic Regression.

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Introduction to survey sampling. Probability sampling techniques. Simple random sampling. Stratified element sampling. Systematic sampling. Equal sized cluster sampling. Unequal sized cluster sampling. PPS selection techniques. Sampling errors.
Survey research project.

Course Content

Introduction to survey research. Survey research methods. Planning of sample surveys. Survey designs. Methods of data collection. Questionnaire design techniques. Fieldwork organization methods. Survey designs over time. Multiplicity survey designs. Establishment survey designs. Components of total survey error. Survey research project.
Prerequisite: STAT 365 or consent of department for non-statistics majors.

Course Content

Rewiev of Probability. Theory Markov Chains. Discrete and Continuous time Markov Chains. Poisson Process. Queuning Processes. Birth and Death Processes. Decision Analysis.

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Course Content

Descriptive and predictive mining. Data preprocessing: cleaning transformation. outlier detection, missing data imputation. Dimension reduction, Principal Component Analysis (PCA). Sampling, oversampling. Exploratory data analysis (EDA). Clustering methods: partitioning, hierarchical, density-based, model-based. Predictive modeling. Regression. Variable selection. Robust and nonlinear regression. Nonparametric regression. Classifiers. Logistic regression. Decision trees. Random Forest. Model evaluation and validation. Real-life applications using recent available software.

Course Content

Types of data. Graphical and tabular represantation of data. Approaches to finding the unexpected in data. Exploratory data analyses for large and high-dimensional data. Analysis of categorical data. Elements of robust estimation. Handling missing data. Smoothing methods. Classification and Clustering applications, Application of Principle Component Analysis and Factor analysis, Use of regression trees for claasification and prediction.

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This course focuses on the following key areas: reading raw data files and Statistical Analysis Software (SAS) data sets, and writing the results to SAS data sets; subsetting data; combining multiple SAS files; creating SAS variables and recording data values; creating listing and summary reports.

Course Content

General introduction to data structures; Statistical data collection and types of business data; Common business problems: customer analytics, segmentation, sales, demand, pricing, fraud, advertisement targeting; Introduction to marketing analytics, definition of marketing terms and definitions, statistical thinking for business problems; Exploratory data analysis and descriptive techniques for business data; Methods for acquiring and manipulating data; Applications of statistical methods for real business cases, machine learning algorithms; Data-centric decision support systems; Statistical applications & discussions.

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Strategies for Experimentation, Randomized Complete and Balanced Incomplete Block Designs, Latin Squares. General, Two-Level and Fractional Factorials. Blocking and Confounding in Two-Level Factorials. Introduction to Response Surface Methodology. Second-Order Experimental Designs. Nonnormal Responses. Unbalanced Data in Factorials. Split-Plot Designs, Nested Designs, Random Effect Models. Repeated Measures.

Course Content

Review of basic statistics. Distribution-free statistics, ranking statistics, U statistics. Large sample theory for U statistics. Tests based on runs. Asymptotic relative efficiency of tests. Hypothesis testing, point and interval estimation. Goodness of fit, rank-order (for location and scale), contingency table analysis and relevant models. Measures of association, analysis of variance.
Prerequisite: Consent of department

Course Content

Populations and samples. Types of biological data. Data transformations. Survival data analysis. Life tables. Sample size determination in clinical trials. Measures of association. The odds ratio and some properties. Application of generalized linear models and logistic regression to biological data. Analysis of data from matched samples.
Prerequisite: STAT 156

Course Content

Reliability studies. Statistical failure models. Censoring and truncation and their types. Useful limit theorems in reliability. Inference procedures for lifetime distributions. System reliability. Bayesian methods. Accelerated life testing.
Prerequisite: STAT 272

Course Content

Basic operations research methodology. Basic models such as network flow models, project scheduling, dynamic programming, and production and inventory control. LP and game theory. Two person zero-sum games and mixed strategies.
Prerequisite: MATH 260

Course Content

Vectoral representation of multivariate data. Sample mean vector and sample covariance matrix. Multivariate distributions, multivariate normal distribution, some other multivariate distributions. Parametric estimation. Hypothesis testing. Reduction of dimensionality.
Prerequisites: MATH 260, STAT 156

Course Content

MANOVA. Principal components, factor analysis. Multivariate classification and clustering. Canonical correlation.
Prerequisite: STAT 465

Course Content

Sample mean vector and sample covariance matrix; matrix decomposition; multivariate normal and Wishart distributions; parameter estimation; hypothesis testing; MANOVA; principal components; factor analysis; multivariate classification and clustering; canonical correlation.

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This course gives insight and a comprehensive introduction to some of the most important quantitative methods and commonly used financial tools required for a thorough understanding of financial markets. Measuring the risk associated with an investment requires being aware of the properties of related statistical estimates. This course will provide these estimates and use of them in financial data along with the study of the models used for financial instruments. It intends to enable students to have access to statistical models and methods to analyze data from financial markets and arbitrage theory for pricing financial instruments and the related mathematical machinery. As a consequence, it aims the students to gain solid background information in the area of finance both for job market and research or personal use.

Course Content

Introduction to decision making and types of decision situations. Bayes theorem and Bayesian decision theory. Prior, posterior and conjugate prior
distributions. Loss functions. Empirical Bayesian approach. Utility theory for decision making. Value of information. Sequential decision procedures. Multidecision problems.
Prerequisite: STAT 154

Course Content

Introduction to concepts of quality and total quality management. Basic principles of teamwork and learning. Probability in Quality Control. Methods and Philosophy of Statistical Process. Control Charts for variables and attributes. Cumulative-Sum and Exponentially Weighted Moving-Average Control Charts. Process Capability Analysis. Introduction to Experimental Design and Factorial Experiments. Taguchi Method, Lot-by-Lot Acceptance Sampling for attributes and by variables.
Prerequisite: STAT 156

Course Content

Introduction to Linear Programming (LP). The simplex method. Transportation, assignment and transshipment problems. Sensitivity testing, duality theory and its applications. Advanced methods in LP and revised simplex algorithm.
Prerequisite: MATH 260

Course Content

Principles of Empirical socio-economic research, formulation of research problems, determination of research design, application of sampling design, strategies of field work, collection of data, improving data quality, selecting appropriate statistical methods, evaluation of test of hypotheses and interpretation of findings, preparation of a research report.

Course Content

Probability distributions and measures of association for count data. Inferences for two-way contingency tables. Generalized linear models, logistic regression and loglinear models. Models with fixed and random effects for categorical data. Model selection and diagnostics when response is categorical. Classification trees.
Prerequisite: STAT 272

Course Content

Basic definition of insurance. Historical background. Insurance applications in government and private sector, regulations and legislation in insurance. Fundamentals of insurance. Types of insurance, disaster insurance and risk menagement applications around the world. Turkish catastrophe insurance pool. Definition of risk, probability aspect of risk. Utility theory, claim processes, distribution of claim processes.
Prerequisite: Consent of the department

Course Content

New advances in the field of statistics.
Prerequisite: Consent of department

Course Content

Applications of different statistical methods in various disciplines such as medicine, science, engineering and social sciences. Presentation of projects involving these applications as group studies.
Prerequisite: STAT 156

Course Content

Time series as a stochastic process. Means, covariances, correlations, stationarity. Moving averages and smoothing. Stationary and nonstationary parametric models. Model specification. Estimation and testing. Seasonality. Some forecasting procedures. Elementary spectral domain analysis. Exponential smoothing methods. Unit root tests.

Course Content

This course is intended to improve the research capabilities of graduating students. Each student will be given a project and an academic advisor; lectures will be given on research design, data evaluation and report writing. A final report and/or seminar is required at the end of the semester.
Prerequisite: Consent of the department.

Course Content

Program of research leading to M.S. degree arranged between student and faculty member. Students register to this course in all semesters starting from the beginning of their second semester while the research program or write-up of thesis is in progress.

Course Content

Probability, random variables, expectations, joint distribution functions, conditional distributions, distribution functions, moment generating functions, order statistics, censoring, limit theorems, multivariate normal distribution.

Course Content

Likelihood theory, sufficiency, point estimation, methods of estimation, unbiasedness, Delta method, hypothesis testing, interval estimation, asymptotic theory, Bayesian statistics, loss function, inference for bivariate distributions.

Course Content

Generalized and conditional inverses, derivatives of quadratic and linear forms, expectation of a matrix. Distributions of quadratic forms. Theory of general linear hypotheses, theory of least squares, full rank and less than full rank models, design models, components of variance models, estimation, hypothesis testing and correlation analysis. Applications to ANOVA and regression. CCH: (1-0) 1.

Course Content

Use of order statistics and other distribution-free statistics for estimation and hypothesis testing, exact non-parametric tests and measures of rank correlation. Relative efficiency, asymptotic relative efficiency and normal-score procedures. Test of goodness of fit.

Course Content

General randomization theory of simple and multistage sampling, sampling with and without replacement and with equal and unequal probabilities, ratio and regression estimates, analytical studies and multiframe problems in relation to stratification, systematic sampling, clustering and double sampling.

Course Content

Markov chains, discrete and continuous Markov processes and associated limit theorems. Poison and birth and death processes. Renewal processes, martingales, Brownian motion, branching processes. Weakly and strongly stationary processes, spectral analysis. Gaussian systems.

Course Content

Research design in the field of statistics following ethical standards, ethical issues in scientific research, how to write a thesis with ethics, journal types, publication types, citations, plagiarism, how to be a graduate student, how to be a researcher.

Course Content

Randomization theory of experimental design. Principles of blocking. General analysis of experimental design models. Construction and analysis of balanced and partially balanced complete and incomplete block designs. Factorial design: confounding, aliasing, fractional replication. Designs for special situations.

Course Content

General regression models, residual analysis, selection of regression models, response surface methods, nonlinear regression models, experimental design and analysis of covariance models. Least squares, Gauss-Markov theorem. Confidence, prediction and tolerance intervals. Simultaneous inference, multiple comparison procedures.

Course Content

Definition of certain fundamental biological and chemical processes, principles of probability and statistics, microarray analyses, fundamental and advanced classification and clustering methods, analyses of protein sequence alignments, structure and elements of biological network, visualization tools and databases in bioinformatics.

Course Content

Seminar course for M.S. students in Statistics.

Course Content

In this course, students will attend the seminars given by academicians or experts who are using Statistics. This course helps graduate students to learn how to conduct a research, how to raise questions and how to be interrogative and how to present their research.

Course Content

In this course, students will present their research. This course helps students to learn how to prepare and do a presentation, to take advantage of receiving the comments of the audience on their research.

Course Content

Introduction to longitudinal data. Exploratory longitudinal data analysis. Missing cases in longitudinal data. Marginal models, transition models, random effects models, multilevel (hierarchical) models. Estimation methods for this type of data. Machine learning techniques for longitudinal data.

Course Content

Probability, combinatorics, random variables, expectations, joint distribution functions, conditional distributions, distribution functions, moment generating functions, limit theorems.

Course Content

Order statistics, exponential families, sufficiency, point estimation, hypothesis testing, interval estimation, confidence intervals.

Course Content

Basics of insurance; Basics of reinsurance; Non-life insurance mathematics; Insurance economics; Risk theory; Individual and collective risk models; Ruin theory; Credibility theory and applications.

Course Content

Overview of statistical distributions, generating random variables, exploratory data analysis, Monte Carlo (MC) method for statistical inference, data partitioning, resampling, bootstrapping, nonparametric density estimation.
Prerequisite: Consent of Department.

Course Content

Bivariate and multivariate smoothing, discovering structure in data, nonparametric regression, Markov Chain Monte Carlo (MCMC), statistical pattern recognition: classifiers and clustering.

Course Content

This course introduces a range of computational techniques that are important to Statistics. The topics covered include introduction to statistical computing, computer arithmetic, numerical linear algebra, regression computations, eigenproblems, numerical optimization, numerical approximations, numerical integration, expectation-maximization (EM) algorithm, basic simulation methodology, Monte Carlo (MC) integration, MC Markov Chain (MCMC) methods.

Course Content

Introduction to categorical response data. Fitting logistic regression models. Interpretation of coefficients. Maximum likelihood estimation. Hypothesis testing. Model building and diagnostics. Polytomous logistic regression. Interaction and confounding. Logistic regression modeling for different sampling designs: case-control and cohort studies, complex surveys. Conditional logistic regression. Exact methods for small samples. Power and sample size. Recent developments in logistic regression approach.

Course Content

Introduction to panel data. Missing cases in panel data. Exploratory panel data analysis. Marginal models, transition models, random effects models, multilevel (hierarchical) models. Estimation methods for this type of data.

Course Content

Transfer function models and cross-spectral analysis, time series regression and GARCH models, vector time series models, error-correction models, cointegration and causality, state space models and Kalman filter, long memory processes, nonlinear processes, temporal aggregation and disaggregation.

Course Content

Introduction to methods for analyzing experimental and observational data. Useful display of univariate and multivariate data. Exploratory data analysis. Transforming data. Detecting and handling outliers. Examining residuals. Resistant lines. Robust estimation. Approaches to handling missing data. Analysis of categorical data. Data mining.

Course Content

Introduction to decision making. Subjective and frequentist probability. Bayes theorem and Bayesian decision theory. Advantages of using a Bayesian approach. Likelihood principle, prior and posterior distributions, conjugate families. Inference as a statistical decision problem. Bayesian point estimation, Tests and confidence regions, model choice, invariance, equivariant estimators, hierarchical and empirical Bayes extensions, robustness and sensitivity, utility and loss, sequential experiments, Markov Chain Monte Carlo Methods, Metropolis-Hastings Algorithm, Gibbs Sampling, The EM Algorithm.

Course Content

Introduction to reliability, order statistics, censoring and likelihood, nonparametric estimation, extreme value theory, failure time distributions, parametric likelihood concepts, simulation-based methods, testing reliability hypothesis, system reliability, failure-time regression analysis, accelerated life testing.

Course Content

Introduction to use of statistical methodology in health related sciences. Types of health data. Odds ratio, relative risk. Prospective and retrospective study designs. Cohort, case-control, case-cohort, nested case-control studies. Analysis of survival data. Kaplan-Meier, life tables, Cox`s proportional hazards model. Analysis of case-control data. Unconditional, conditional, polytomous logistic regression. Introduction to genetic epidemiology. Testing Hardy-Weinberg law. Linkeage analysis. Analysis of microarray data. Association studies. Sample size and power. Recent developments in biostatistics and genetic epidemiology.

Course Content

Key aspects of statistical consulting and data analysis activities. Formulation of statistical problems from client information. Analysis of complex data sets. Case studies. Writing and presenting reports.

Course Content

Structures, semi-structured and unstructured data types. Data manipulation and preprocessing. Dimension reduction. Sampling, oversampling, undersampling. Data scraping and wrangling. Visualization of multivariate data. Panel displays, surface plots, 3D scatterplots, contour plots. 2D representation of multivariate data. Interactive graphics revealing any structure in data: Asimov?s grand tour, projection pursuit explanatory data analysis (PPEDA). Visualization of categorical data. Dynamic graphics.

Course Content

Unsupervised learning. Principal component analysis (PCA), clustering methods. Rule learning, association rules. Supervised learning. Multiple linear regression. K-nearest neighbors. Logistic regression. Linear discriminant analysis. Linear model selection. Regularization techniques. Ridge regression, LASSO. Splines. Generalized additive models (GAMs). Tree-based methods. Ensemble learning. Bagging, random forest, boosting. Support vector machines. Neural networks and deep learning. Evaluating the performance of machine learning algorithms. No Free Lunch theorems. Bias-variance decomposition. Bagging. Boosting. Generative adversarial networks (GANs). Autoencoders. Variational Autoencoders.

Course Content

Introduction to history and concepts of probability, statistics and data science, artificial intelligence, data mining, machine learning, deep learning, comparison of these topics, probability, combinatorics, random variables, some common discrete and continuous distributions and their properties, expectations, joint distribution functions, conditional distributions, distribution functions, moment generating functions, transformations of variables, multivariate normal distribution, limit theorems.

Course Content

Order statistics. Likelihood functions and likelihood theory. Exponential families. Point estimation. Some properties of estimators. Interval estimation. Hypothesis testing.

Course Content

Program of research leading to Ph.D. degree arranged between student and a faculty member. Students register to this course in all semesters starting from the beginning of their second semester while the research program or write-up of thesis is in progress.

Course Content

Notions of measure theory. General concepts and tools of probability theory. Independence; convergence; laws of large number. Random walks.

Course Content

Concept of conditioning. From independence to dependence. Ergodic theorems. Martingales and decomposability. Brownian motion and limit distributions.

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Advanced topics in linear and non-linear statistical estimation.

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Advanced topics in statistical hypothesis testing.

Course Content

Balanced and partially balanced incomplete block designs. Mixture designs. Factorial designs. Response surfaces. Optimal allocation of observations.

Course Content

Rank testing and estimation procedures. Locally most powerful rank tests. Criteria for unbiasedness. Exact and asymptotic distribution theory. Asymptotic efficiency. Rank correlation. Sequential procedures.

Course Content

Discrete and continuous time Markov chains and Brownian motion. Gaussian processes, queues, epidemic models, branching processes, renewal processes.

Course Content

Sequential probability ratio test. Approximations for stopping boundaries. Power curve and expected stopping time. Wald s lemmas. Bayes character of SPRT. Composite hypothesis. Ranking and selection.

Course Content

Advanced topics in multivariate statistical analysis.

Course Content

Advanced topics in life models, reliability and hazard functions. Decision making in life testing. Design of experiments in life testing.

Course Content

Application of statistical theory and procedures to various types of data. Use of computers and numerical methods are emphasized.

Course Content

Continuation of 2460614

Course Content

A strong background in control charts including adaptations, acceptance sampling for attributes and variables data. Acceptance plans. Statistics of combinations.

Course Content

Large sample properties of tests and estimates. Problems of consistency and various forms of asymptotic efficiencies. Irregular estimation problems. Inference from stochastic processes.

Course Content

Two level factorial and fractional factorial designs, blocking, polynomial models, first order and second order designs, several responses, determination and optimum conditions, design criteria involving variance and bias.

Course Content

Development of linear classification models, components of variance for balanced designs, polynomial models, harmonic regression, crossed models for combined qualitative and quantitative factors. Analysis of variance for fixed, random and mixed effects models. Randomization. Violation of assumptions.

Course Content

Transforming data. More refined estimators. Comparing location estimators. M and L estimators. Robust scale estimators and confidence intervals. Relevance to hypothesis testing. CCH: (1-0)1.

Course Content

Structural models for counted data, maximum likelihood estimates for complete tables, formal goodness of fit; summary statistics and model selection, maximum likelihood estimates for incomplete tables, estimating the size of a closed population, models for measuring change, analysis of square tables; symmetry and marginal homogeneity, measures of association and agreement, Pseudo-Bayes estimates of cell probabilities, asymptotic methods. CCH: (1-0)1.
Prerequisite: Consent of Department.

Course Content

Purely spatial processes. Spatial autocorrelation. Distribution theory for spatial statistics. Analysis for point patterns. Parametric spatial models. Estimation and testing procedures. CCH: (1-0)1.

Course Content

Several advanced topics of statistical inference suited to the needs of researcher.

Course Content

Special models. Large sample theory for discrete and continuous parameter stochastic processes. Optimal testing. Bayesian, nonparametric and sequential inference for stochastic processes. Martingales. Stochastic differential equations.

Course Content

Second moment models of random variables and vectors. Correlation theory of random processes in the time and frequency domains. Theory of random fields in the time and frequency domains. Crossings and extremes of random functions. Applications.

Course Content

Exploring multidimensional data. Discovering structure in data. Bootstrapping basics and dependent data. Data partitioning. Statistical pattern recognition: classifiers and clustering. Bivariate and multivariate smoothing. Nonparametric regression models. Advanced topics in Markov Chain Monte Carlo (MCMC).

Course Content

Review of matrix algebra. A theoretical development of generalized linear models. Estimation, interpretation and inferences for generalized linear models for responses from different distributions, such as Gaussian, Binomial, Poisson. Loglinear models. Penalized estimation.

Course Content

This is an introductory stochastic calculus and stochastic differential equations course which will cover fundamental concept in the areas of stochastic calculus in finance, population models and such.

Course Content

Seminar course for Ph.D. students in Statistics.

Course Content

In this course, students will attend the seminars given by academicians or experts who are using Statistics. This course helps graduate students to learn how to conduct a research, how to raise questions and how to be interrogative and how to present their research.

Course Content

In this course, students will present their research. This course helps students to learn how to prepare and do a presentation, to take advantage of receiving the comments of the audience on their research.

Course Content

This is an introductory measure-theoretic probability theory course. We will be covering the fundamental concepts in Statistics to the full extent, such as Law of Large Numbers, Central Limit Theorem, Convergence, Dependence, Independence, Conditional Expectation. The course covers all the content that appear on the Probability Qualification Examination.

Course Content

Theory of likelihood based estimation, likelihood construction for advance models, theory of robust estimation, theory of hypothesis testing

Course Content

Visualizations of multivariate data. Biplots. Introduction to correspondence analysis. theory and applications of correspondence analysis. Computation of correspondence analysis. Multiple correspondence analysis. Related methods: principal component analysis, log-ratio analysis, and discriminant analysis. Applications in a varity of areas: social sciences, genetics, linguistics,...

Course Content

Course Content

The course is divided into two parts: Hidden Marko models (HMMs) and methods for handling so called big data sets that fall under the heading of machine learning. First part covers model specification, parameter estimation (maximum likelihood using numerical maximization and the EM algorithm), model selection and checking, forecasting, local and global decoding (the Viterbi algorithm), state prediction. Several non-standard HMMs, HMM approximations to hidden semi-Markov models and to continuous state-space processes. Topics covered include supervised and unsupervised learning using various of methods of clustering, dimension reduction, tree-based and non-parametric regression.

Course Content

Definition of certain fundamental biological and chemical processes, principles of probability and statistics, microarray analyses, fundamental and advanced classification and clustering methods, analyses of protein sequence alignments, structure and elements of biological network, visualization tools and databases in bioinformatics.

Course Content

Problem of missing data; missing data patterns and mechanisms; single and multiple imputation methods; likelihood based inference; Bayesian simulation based inference; data augmentation; missing data handling for correlated, longitudinal, and clustered data; missing data analysis in SAS and R.

Course Content

This course is constructed from seminars that will be organised by Graduate School of Natural and Applied Sciences. The seminars will cover technical, cultural, social and educational issues to prepare the graduate students following the PhD programs.

Course Content