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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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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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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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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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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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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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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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Stochastic environmental research and risk assessment (Print), 2022
S. Saha +5 more
semanticscholar +1 more source
S. Saha +5 more
semanticscholar +1 more source
2010
This chapter initially discusses topics like deviances, hypothesis testing and estimation of the dispersion parameter. The interpretation of deviances as measures of goodness-of-fit is highlighted. Next comes asymptotic normality of the estimators, the construction of confidence intervals and the role played by the Fisher information.
Esbjörn Ohlsson, Björn Johansson
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This chapter initially discusses topics like deviances, hypothesis testing and estimation of the dispersion parameter. The interpretation of deviances as measures of goodness-of-fit is highlighted. Next comes asymptotic normality of the estimators, the construction of confidence intervals and the role played by the Fisher information.
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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1994
Abstract Generalized linear models (GLM) are an extension of the linear regression models beyond the realm of the normal distribution. Their unified formulation, as opposed to a set of distinct methods for different distributional assumptions, is due to Nelder and Wedderburn (1972) and Wedderburn (1974).
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Abstract Generalized linear models (GLM) are an extension of the linear regression models beyond the realm of the normal distribution. Their unified formulation, as opposed to a set of distinct methods for different distributional assumptions, is due to Nelder and Wedderburn (1972) and Wedderburn (1974).
openaire +1 more source