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Course Criteria
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4.00 Credits
Supervised, applied project based on stochastic modeling of scientific data. Offered: Sp.
Prerequisite:
STAT 517
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3.00 Credits
Descriptive techniques. Stationary and nonstationary processes, including ARIMA processes. Estimation of process mean and autocovariance function. Fitting ARIMA models to data. Statistical tests for white noise. Forecasting. State space models and the Kalman filter. Robust time series analysis. Regression analysis with correlated errors. Statistical properties of long memory processes. Offered: A.
Prerequisite:
STAT 513
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4.00 Credits
Estimation of spectral densities for single and multiple time series. Nonparametric estimation of spectral density, cross-spectral density, and coherency for stationary time series, real and complex spectrum techniques. Bispectrum. Digital filtering techniques. Aliasing, prewhitening. Choice of lag windows and data windows. Use of the fast Fourier transform. The parametric autoregressive spectral density estimate for single and multiple stationary time series. Spectral analysis of nonstationary random processes and for randomly sampled processes. Techniques of robust spectral analysis. Offered: jointly with E E 520; W.
Prerequisite:
one of STAT 342, STAT 390, STAT 481, or IND E 315
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3.00 Credits
Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Offered: jointly with MATH 521; A.
Prerequisite:
either MATH 426 or MATH 576
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3.00 Credits
Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Offered: jointly with MATH 522; W.
Prerequisite:
either MATH 426 or MATH 576
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3.00 Credits
Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Offered: jointly with MATH 523; Sp.
Prerequisite:
either MATH 426 or MATH 576
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3.00 Credits
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 students in biostatistics and other scientific fields. Offered: jointly with BIOST 524; even years.
Prerequisite:
BIOST 511 or equivalent, and one of STAT 421, STAT 423, BIOST 513, BIOST 518, or EPI 512; or permission of instructor
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3.00 Credits
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. Offered: jointly with BIOST 529/CS&SS 529.
Prerequisite:
either STAT 421, STAT 423, STAT 504, QMETH 500, BIOST 511, or BIOST 517, or equivalent; or permission of instructor
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3.00 Credits
Review of spectral analysis. Theory of continuous and discrete wavelets. Multiresolution analysis. Computation of discrete wavelet transform. Time-scale analysis. Wavelet packets. Statistical properties of wavelet signal extraction and smoothers. Estimation of wavelet variance. Offered: jointly with E E 24; Sp.
Prerequisite:
some Fourier theory and linear algebra; Math or STAT 390, ECON or STAT 481, or STAT 513; or IND E 315
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3.00 Credits
Introduction to one-, two-way analysis of variance; randomized blocks; fixed, random effects, multiple comparisons. Statistical distribution theory for quadratic forms of normal variables. Fitting of the general linear model by least squares. Offered: jointly with BIOST 533; Sp.
Prerequisite:
STAT 421 or STAT 423; and STAT 513, BIOST 515, and a course in matrix algebra
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