Results 221 to 230 of about 25,059,290 (251)
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Introduction to the generalized linear model (GLM): comparing two groups in a Poisson regression
Marc Kery, Kenneth F Kellnerexaly +2 more sources
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
In designed experiments, we often encountered nonnormal response variables. The data transformations (Transf) approached are frequently employed to deal with these problems. One has to realize that analyzing such data based on transformations posed many drawbacks. A better approach in dealing with these problems is by using the Generalized Linear Model
exaly +3 more sources
In designed experiments, we often encountered nonnormal response variables. The data transformations (Transf) approached are frequently employed to deal with these problems. One has to realize that analyzing such data based on transformations posed many drawbacks. A better approach in dealing with these problems is by using the Generalized Linear Model
exaly +3 more sources
2021
The particulate matters (PM10 and PM2.5) inside urban subway stations greatly influence indoor air quality and passenger comfort. This study aims to analyze and interpret the concentrations of PM10 and PM2.5, measured in several subway stations from October 9th to 22nd, 2016 in Beijing, China.
Xinru Wang +4 more
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The particulate matters (PM10 and PM2.5) inside urban subway stations greatly influence indoor air quality and passenger comfort. This study aims to analyze and interpret the concentrations of PM10 and PM2.5, measured in several subway stations from October 9th to 22nd, 2016 in Beijing, China.
Xinru Wang +4 more
openaire +1 more source
Quality and Reliability Engineering International, 2007
AbstractThe data‐transformation approach and generalized linear modeling both require specification of a transformation prior to deriving the linear predictor (LP). By contrast, response modeling methodology (RMM) requires no such specifications. Furthermore, RMM effectively decouples modeling of the LP from modeling its relationship to the response ...
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AbstractThe data‐transformation approach and generalized linear modeling both require specification of a transformation prior to deriving the linear predictor (LP). By contrast, response modeling methodology (RMM) requires no such specifications. Furthermore, RMM effectively decouples modeling of the LP from modeling its relationship to the response ...
openaire +2 more sources
Statistical analysis of stability data by means of general linear models (GLM)
Drug Development and Industrial Pharmacy, 1991AbstractApplication of a General Linear Model (GLM, “Analysis of Covariance”) to the statistical interpretation of stability data combines the methods of regression and analysis of variance in one common model. Expanding the well accepted method of linear regression upon time, the GLM model permits one to include supportive factors which may be either ...
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AIP Conference Proceedings, 2019
At vehicle insurance companies, the determination of the appropriate pure premium will make the business run well. In this study, we were modeling claims frequency data by considering the characteristics of policyholder such as policyholder’s age, marital status, sex, car engine capacity, and age.
null Jamilatuzzahro +3 more
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At vehicle insurance companies, the determination of the appropriate pure premium will make the business run well. In this study, we were modeling claims frequency data by considering the characteristics of policyholder such as policyholder’s age, marital status, sex, car engine capacity, and age.
null Jamilatuzzahro +3 more
openaire +1 more source
2014
General circulation model (GCM) climate projections cannot be relied on to provide information at scales finer than the GCM model-grid resolutions; hence, fine-scale information can be achieved by the use of high spatial resolution in dynamical models or empirical statistical downscaling.
Kigobe, M., Wheater, H., McIntyre, N.
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General circulation model (GCM) climate projections cannot be relied on to provide information at scales finer than the GCM model-grid resolutions; hence, fine-scale information can be achieved by the use of high spatial resolution in dynamical models or empirical statistical downscaling.
Kigobe, M., Wheater, H., McIntyre, N.
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1987
It is well recognized that there are important relationships between quantitative or quantified geo-variates and the geological, geophysical and geochemical environment of mineral deposits, some of which are diagnostic of the occurrence of ore. However, since there is still much uncertainty as to the precise nature of these relationships, we are ...
T. K. Wignall, J. De Geoffroy
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It is well recognized that there are important relationships between quantitative or quantified geo-variates and the geological, geophysical and geochemical environment of mineral deposits, some of which are diagnostic of the occurrence of ore. However, since there is still much uncertainty as to the precise nature of these relationships, we are ...
T. K. Wignall, J. De Geoffroy
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Application of General Linear Modeling (GLM) to analysis of indentation in compact bone
2012The indentation process is useful in determining the behavior of a material under the action of a point or line load. The main geometric features of a wedge shaped indenter are the wedge angle and the radius on the wedge apex. General Linear Modeling is a statistical linear regression model which may be stated as: Y= β0+β1Xi1+β2X i2+β3Xi3+β4X i1Xi2 ...
Reilly, Ger, Taylor, David
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PREMIUM PRICING AND RISK ASSESSMENT FOR CLAIM AMOUNTS BASED ON GENERALIZED LINEAR MODELS (GLM)
2013ActuarialScience is described as a mechanism that decreases the negative financialeffects of random events which becomes obstacles to actualize reasonableexpectations. It is important subject to make a fair share for the same amountof money which is paid by the people who has the same risk.
Sarul, Latife Sinem +1 more
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