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Chapter 12
Chapter 12

ON SUCCESSIVE SAMPLING AND FIXED INCLUSION
ON SUCCESSIVE SAMPLING AND FIXED INCLUSION

... Abstract. In the method of successive sampling, a sample of n distinct units is drawn from a population of N units one at a time. Each unit i is drawn with a fixed selection probability pi and any repeated units are ignored. When the pi are proportional to a given size measure, this is called probabi ...


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The Laws of Probability and the Law of the Land
The Laws of Probability and the Law of the Land

... Only Brilmayer and Kornhauser take as their explicit target the use at trial of Bayes's Theorem. Although they acknowledge the assistance of Shafer in preparing their article, it is not clear that Shafer, as a working statistician, would deny the appropriateness of using ordinary probability calcula ...
Chapter 6
Chapter 6

Thermodynamic Analysis of Interacting Nucleic Acid Strands
Thermodynamic Analysis of Interacting Nucleic Acid Strands

... specification of polynomial-time MFE determination [30, 1] and partition function [10, 11] algorithms. Although pseudoknots exist in nature [43] and have been incorporated in synthetic DNA systems [46, 42], many natural and synthetic structures of interest do not include pseudoknots [23, 32, 38], and ...
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Explanatory Variable/Error Term Independence Premise
Explanatory Variable/Error Term Independence Premise

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Introduction - ODU Computer Science

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Link (PDF, 5.57 MB) (PDF, 5441 KB)

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MAITA Project CyberPanel review

... • To modify/observe a component find a residence of the component and modify/observe it in the residence • To modify/observe a component find a migration path and modify/observe it during the transmission Maita Final, Dec. 5, 2002 -- **Not for distribution** ...
Stability Analysis of Mean-CVaR Investment Model with Transaction
Stability Analysis of Mean-CVaR Investment Model with Transaction

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Transforming Probabilities with Combinational Logic
Transforming Probabilities with Combinational Logic

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Generalized linear model

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.
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