'Minimum variance unbiased estimator' diaporamas de présentation

Basic Statistical Concepts

Basic Statistical Concepts. Donald E. Mercante, Ph.D. Biostatistics School of Public Health L S U - H S C. Sample. Population. Parameters. Statistics. Two Broad Areas of Statistics. Descriptive Statistics - Numerical descriptors - Graphical devices - Tabular displays

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STATISTICAL INFERENCE PART II SOME PROPERTIES OF ESTIMATORS

STATISTICAL INFERENCE PART II SOME PROPERTIES OF ESTIMATORS. SOME PROPERTIES OF ESTIMATORS. ?: a parameter of interest; unknown Previously, we found good(?) estimator(s) for ? or its function g(?). Goal: Check how good are these estimator(s). Or are they good at all?

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Options for Identifying & Quantifying Pollutant Loads

Options for Identifying & Quantifying Pollutant Loads . Presentation Overview. Goals of pollutant load estimation Options for quantifying current loads or conditions Data-driven approaches Models Modeling, as it relates to environmental systems Types of models

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Previous Lecture: Distributions

Previous Lecture: Distributions. This Lecture. Introduction to Biostatistics and Bioinformatics Estimation I. By Judy Zhong Assistant Professor Division of Biostatistics Department of Population Health Judy.zhong@nyumc.org.

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STATISTICAL INFERENCE PART II POINT ESTIMATION

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Point Estimation of Parameters and Sampling Distributions

Point Estimation of Parameters and Sampling Distributions. Outlines: Sampling Distributions and the central limit theorem Point estimation Methods of point estimation Moments Maximum Likelihood. Sampling Distributions and the central limit theorem. Random Sample

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Taking Uncertainty Into Account: Bias Issues Arising from Uncertainty in Risk Models

Taking Uncertainty Into Account: Bias Issues Arising from Uncertainty in Risk Models. John A. Major, ASA Guy Carpenter & Company, Inc. Example: Exponential Distribution. N=20 observations T = sample mean; l =1 true mean MLE EP curve: q-exceedance point (PML, VaR) X .01 = 4.605 actual.

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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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The Likelihood Function - Introduction

The Likelihood Function - Introduction. Recall: a statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution that produced the data. The distribution f ? can be either a probability density function or a

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The Likelihood Function - Introduction

The Likelihood Function - Introduction. Recall: a statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution that produced the data. The distribution f ? can be either a probability density function or a

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t distribution

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