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Sexual Abuse, 2013 The good lives model (GLM) has become an increasingly popular theoretical framework underpinning sex offender treatment programs, and preliminary research suggests that the GLM may enhance the efficacy of programs that adhere to the Risk, Need, and Responsivity (RNR) principles. However, this potential rests on the appropriate operationalization of the
Gwenda M, Willis +2 more
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GLM and joint GLM techniques in hydrogeology: an illustration
International Journal of Hydrology Science and Technology, 2012In regression models with positive observations, estimation is often based on either the log-normal or the gamma model. Generalised linear models and joint generalised linear models are appropriate for analysing positive data with constant and non-constant variance, respectively.
Rabindra Nath Das, Jinseog Kim
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2021
GLMs with a binomial distribution are designed for the analysis of binomial counts (how many times something occurred relative to the total number of possible times it could have occurred). A logistic link function constrains predictions to be above zero and below the maximum using the S-shaped logistic curve.
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GLMs with a binomial distribution are designed for the analysis of binomial counts (how many times something occurred relative to the total number of possible times it could have occurred). A logistic link function constrains predictions to be above zero and below the maximum using the S-shaped logistic curve.
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Crystallization of the glmS Ribozyme-Riboswitch
2009Procedures that were critical for crystallization of the glmS ribozyme-riboswitch RNA domain from the thermophilic Gram-positive bacterium Thermoanaerobacter tengcongensis are described. Experimental design based on screening multiple variant RNA sequences and techniques used to identify initial crystallization conditions were similar to those employed
Daniel J, Klein +1 more
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2021
Binomial GLMs can also be used to analyse binary data as a special case, with some minor differences introduced into the analysis by the constrained nature of the binary data.
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Binomial GLMs can also be used to analyse binary data as a special case, with some minor differences introduced into the analysis by the constrained nature of the binary data.
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2000
As we did in the previous chapter, we give a number of applications of the major results obtained in Chapter 2. We do so for the General Linear Structural Econometric Model (GLSEM), an important topic for many fields but especially for econometrics.
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As we did in the previous chapter, we give a number of applications of the major results obtained in Chapter 2. We do so for the General Linear Structural Econometric Model (GLSEM), an important topic for many fields but especially for econometrics.
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2017
This chapter contains some extensions of the multiple linear regression model. See Definition 1.1 for the 1D regression model , sufficient predictor (SP = h(x)), estimated sufficient predictor (\(ESP =\hat{ h}(\mathbf{x})\)), generalized linear model (GLM), and the generalized additive model (GAM).
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This chapter contains some extensions of the multiple linear regression model. See Definition 1.1 for the 1D regression model , sufficient predictor (SP = h(x)), estimated sufficient predictor (\(ESP =\hat{ h}(\mathbf{x})\)), generalized linear model (GLM), and the generalized additive model (GAM).
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The Basics of Pricing with GLMs
2010In non-life insurance pricing we determine how one or more key ratios Y vary with a number of rating factors. This is reminiscent of analyzing how the dependent variable Y varies with the covariates x in a multiple linear regression. In this chapter we introduce the class of Generalized Linear Models (GLMs), which generalizes the linear regression ...
Esbjörn Ohlsson, Björn Johansson
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Functional programming for GLMs
1989The statistician of the 21st century will have been educated in a modern computing environment and will expect statistical modelling software to reflect recent advances in computer technology. Existing statistical software and the current languages used for statistical analysis are based on somewhat old-fashioned computing concepts.
Michael Clarke +3 more
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