'Maximum likelihood estimators' diaporamas de présentation

RW3: Uncorrelated Increments

RW3: Uncorrelated Increments. Tests of Serial Correlation. Tests of Serial Correlation. Under the weakest version of the RW theory, RW3, the increments of the random walk are uncorrelated at all leads and lags.

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RW3: Uncorrelated Increments

RW3: Uncorrelated Increments. Tests of Serial Correlation. Tests of Serial Correlation. Under the weakest version of the RW theory, RW3, the increments of the random walk are uncorrelated at all leads and lags.

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Lecture 3

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Maximum Likelihood Estimation

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CARe Seminar 2005 Medical Malpractice: Observations, Issues and Methodologies June 7, 2005 Hamilton, Bermuda

CARe Seminar 2005 Medical Malpractice: Observations, Issues and Methodologies June 7, 2005 Hamilton, Bermuda. Panelists :James D. Hurley, ACAS, MAAA Towers Perrin Kirk Bitu, FCAS, MAAA Willis Re Moderator : Anne Petrides, FCAS, MAAA Meetinghouse LLC. Session Format.

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Empirical Similarity and Objective Probabilities

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Introduction to Statistics ? Day 4

Introduction to Statistics ? Day 4. Lecture 1 Probability Random variables, probability densities, etc. Lecture 2 Brief catalogue of probability densities The Monte Carlo method. Lecture 3 Statistical tests Fisher discriminants, neural networks, etc

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Sampling Distributions & Point Estimation

Sampling Distributions & Point Estimation. Questions. What is a sampling distribution? What is the standard error? What is the principle of maximum likelihood? What is bias (in the statistical sense)? What is a confidence interval? What is the central limit theorem?

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Algorithms for Smoothing Array CGH data

Algorithms for Smoothing Array CGH data. Kees Jong (VU, CS and Mathematics) Elena Marchiori (VU, Computer Science) Aad van der Vaart (VU, Mathematics) Gerrit Meijer (VUMC) Bauke Ylstra (VUMC) Marjan Weiss (VUMC). Tumor Cell. Chromosomes of tumor cell:. CGH Data. ? C o p y #.

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9. Binary Dependent Variables

9. Binary Dependent Variables. 9.1 Homogeneous models Logit, probit models Inference Tax preparers 9.2 Random effects models 9.3 Fixed effects models 9.4 Marginal models and GEE Appendix 9A - Likelihood calculations. 9.1 Homogeneous models.

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Introduction to Statistics ? Day 4

Introduction to Statistics ? Day 4. Lecture 1 Probability Random variables, probability densities, etc. Lecture 2 Brief catalogue of probability densities The Monte Carlo method. Lecture 3 Statistical tests Fisher discriminants, neural networks, etc Goodness-of-fit tests

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The Very Basics of Detection Theory

The Very Basics of Detection Theory. J. Scott Tyo tyo@optics.arizona.edu. The Roots of Detection Theory. SDT goes back to the early days of RADAR signal processing Broadly applicable in all areas of sensing Remote Sensing Medical Imagery Communications

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Econometrics 1

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Statistical Data Analysis: Lecture 8

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Conditional Expectation

Conditional Expectation. For X, Y discrete random variables, the conditional expectation of Y given X = x is and the conditional variance of Y given X = x is where these are defined only if the sums converges absolutely. In general,.

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Conditional Expectation

Conditional Expectation. For X, Y discrete random variables, the conditional expectation of Y given X = x is and the conditional variance of Y given X = x is where these are defined only if the sums converges absolutely. In general,.

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Maximum Likelihood

Maximum Likelihood. There are three major paradigms of estimating linear models Method of Moments Oldest estimation method Population moments are best estimated by sample moments Not too useful for complex estimation Least Squares Minimize the sum of the squared errors

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Today

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??? Estimation

??? Estimation. Methods of estimation (MOM, Least squares, MLE) Sample properties of estimators Confidence intervals. A. METHODS OF ESTIMATION. METHOD OF MOMENTS suppose x 1 , x 2 , ... are iid. Fix k a positive integer. Then are iid and by the weak law of large numbers

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