'Sufficient statistic' presentation slideshows

730 Lecture 7

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SOME GENERAL PROBLEMS

SOME GENERAL PROBLEMS. Problem.

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SOME GENERAL PROBLEMS

SOME GENERAL PROBLEMS. Problem.

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A Study of Estimation Methods for Defect Estimation

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BRAID: Stream Mining through Group Lag Correlations

BRAID: Stream Mining through Group Lag Correlations. Yasushi Sakurai Spiros Papadimitriou Christos Faloutsos SIGMOD 2005. Introduction. Lag correlations : For example: Higher amounts of fluoride in water ? fewer dental cavities some years later Goal :

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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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STATISTICAL INFERENCE PART II POINT ESTIMATION

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Consistency

Consistency. An estimator is a consistent estimator of ?, if , i.e., if converge in probability to ?. Theorem. An unbiased estimator for ?, is a consistent estimator of ? if Proof:. Example.

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Consistency

Consistency. An estimator is a consistent estimator of ?, if , i.e., if converge in probability to ?. Theorem. An unbiased estimator for ?, is a consistent estimator of ? if Proof:. Example.

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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

131 views • 11 slides