Results 261 to 270 of about 11,154,365 (309)
Some of the next articles are maybe not open access.
1981
In Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves to normally distributed random variables, and to cases where the model is linear in the unknown parameters.
openaire +1 more source
In Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves to normally distributed random variables, and to cases where the model is linear in the unknown parameters.
openaire +1 more source
2014
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
openaire +2 more sources
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
openaire +2 more sources
2008
A partially linear model requires the regression function to be a linear function of a subset of the variables and a nonparametric non-specified function of the rest of the variables. Suppose, for example, that one is interested in estimating the relationship between an outcome variable of interest y and a vector of variables (x, z).
openaire +1 more source
A partially linear model requires the regression function to be a linear function of a subset of the variables and a nonparametric non-specified function of the rest of the variables. Suppose, for example, that one is interested in estimating the relationship between an outcome variable of interest y and a vector of variables (x, z).
openaire +1 more source
American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Tomasz Burzykowski +3 more
openaire +2 more sources
Tomasz Burzykowski +3 more
openaire +2 more sources
Linear and Non-linear Modeling
2011This chapter describes some of the tools that are available in R for fitting certain kinds of conditional distributions; that is, constructing models to predict the behavior of one random variable given that the value of another one or more is known. Examples of such models in forestry include height-diameter models, diameter-volume models, and so on ...
Andrew P. Robinson, Jeff D. Hamann
openaire +1 more source
2005
A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The counts are modeled by the Poisson distribution, and related to the classifying variables through a ...
openaire +1 more source
A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The counts are modeled by the Poisson distribution, and related to the classifying variables through a ...
openaire +1 more source
The American Statistician, 1994