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*  Least squares inference in phylogeny
Sometimes the variances may not be known, but they can be modeled as a function of the distance estimates. In the Fitch and ... The evaluation of S for a given topology (which includes the computation of the branch lengths) is a linear least squares ...
*  Brain morphometry
Of course, multiple solutions exist for such non-linear warping procedures, and to balance appropriately between the ... which can be modeled. In the following, the image is segmented into non-brain and brain tissue, with the latter usually being ... highly non-linear) methods, and templates can be generated from the study's pool of brains, from a brain atlas or a derived ... highly non-linear registration algorithms are used, and the statistical analyses are not performed on the registered voxels but ...
*  List of important publications in statistics
Importance: Includes a large body of research on Bayesian analysis for outlier problems, variance components, linear models and ... JSTOR 2985181 Description: Seminal paper introducing semi-parametric proportional hazards models (Cox models) for survival data ... First description of three methods of estimation of variance components in mixed linear models for unbalanced data. "One of the ... Early exposition of the general linear model using matrix algebra (following lecture notes of George W. Brown). Bases inference ...
*  Animal breeding
Applications of linear models in animal breeding, Guelph, Ont: University of Guelph, ISBN 0-88955-030-1 Hammond K. Gianola, D ( ... Linear models for the prediction of animal breeding values, Oxon: CAB International, ISBN 0-85198-996-9 Cameron, N. D. (1997), ... Animal breeding is a branch of animal science that addresses the evaluation (using best linear unbiased prediction and other ...
*  Linear model
General linear model Generalized linear model Linear system Statistical model Priestley, M.B. (1988) Non-linear and Non- ... An example of a linear time series model is an autoregressive moving average model. Here the model for values {Xt} in a time ... Note that here the "linear" part of the term "linear model" is not referring to the coefficients φi and θi, as it would be in ... "linear model" refers to the structure of the above relationship in representing Xt as a linear function of past values of the ...
*  General linear model
The general linear model or multivariate regression model is a statistical linear model. It may be written as Y = X B + U , {\ ... ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression model ... generalized linear models may be used to relax assumptions about Y and U. The general linear model incorporates a number of ... Bayesian multivariate linear regression Comparison of general and generalized linear models K. V. Mardia, J. T. Kent and J. M. ...
*  Generalized linear model
... two broad statistical models. The general linear model may be viewed as a special case of the generalized linear model with ... very important example of a generalized linear model (also an example of a general linear model) is linear regression. In ... Such a model is a log-odds or logistic model. Generalized linear models cover all these situations by allowing for response ... The term "generalized linear model", and especially its abbreviation GLM, are sometimes confused with general linear model. Co- ...
*  Log-linear model
Log-linear analysis General linear model Generalized linear model Boltzmann distribution. ... A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the ... Poisson regression for contingency tables, a type of generalized linear model. The specific applications of log-linear models ... while c and the wi stand for the model parameters. The term may specifically be used for: A log-linear plot or graph, which is ...
*  Proper linear model
In statistics, a proper linear model is a linear regression model in which the weights given to the predictor variables are ... Unit-weighted regression is the most common example of an improper linear model. Dawes, R. M. (1979). "The robust beauty of ... Simple regression analysis is the most common example of a proper linear model. ... improper linear models in decision making". American Psychologist. 34 (7): 571-582. doi:10.1037/0003-066X.34.7.571. ...
*  Hierarchical generalized linear model
Moreover, the generalized linear mixed model (GLMM) is a special case of the hierarchical generalized linear model. In ... There are different techniques to fit a hierarchical generalized linear model. Hierarchical generalized linear model have been ... In statistics, hierarchical generalized linear models (HGLM) extend generalized linear models by relaxing the assumption that ... then we call the model Beta conjugate model. Moreover, the mixed linear model is in fact the normal conjugate HGLM. A summary ...
*  Vector generalized linear model
For example, in discrete choice models, one has conditional logit models, nested logit models, generalized logit models, and ... In statistics, the class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by ... and include 3 of the most important statistical regression models: the linear model, Poisson regression for counts, and ... therefore this model is also called the cumulative probit model. In general they are called cumulative link models. For ...
*  Linear model of innovation
The Linear Model of Innovation is an early model of innovation that suggests technical change happens in a linear fashion from ... The linear models of innovation supported numerous criticisms concerning the linearity of the models. These models ignore the ... Two versions of the linear model of innovation are often presented: "technology push" model "market pull" model. From the 1950s ... A history of the linear model of innovation may be found in Godin (2006) Innovation Technological change Science and technology ...
*  Generalized functional linear model
The generalized functional linear model (GFLM) is an extension of the generalized linear model (GLM) that allows one to regress ... Functional additive models Functional data analysis Functional principal component analysis Generalized linear model Karhunen- ... James (2002). "Generalized linear models with functional predictors". Journal of the Royal Statistical Society, Series B. 64 (3 ... truncated model i.e. the linear predictor trunctated to the summation of the first p {\displaystyle p} components, is a ...
*  Comparison of general and generalized linear models
McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412- ... McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412- ...
*  Linear probability model
... "linear probability model", this relationship is a particularly simple one, and allows the model to be fitted by simple linear ... In statistics, a linear probability model is a special case of a binomial regression model. Here the dependent variable for ... models such as the logit model or the probit model are more commonly used. Cox, D. R. (1970). "Simple Regression". Analysis of ... ISBN 0-631-13345-3. Wooldridge, Jeffrey M. (2013). "A Binary Dependent Variable: The Linear Probability Model". Introductory ...
*  Generalized linear array model
Such models provide a structure and a computational procedure for fitting generalized linear models or GLMs whose model matrix ... It based on the generalized linear model with the design matrix written as a Kronecker product. The generalized linear array ... Currie, I. D.; Durban, M.; Eilers, P. H. C. (2006). "Generalized linear array models with applications to multidimensional ... In statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. ...
*  Linear transform model (MRI)
The linear transform model is a common and widespread assumption used in the interpretation of fMRI studies. However, some ... The linear transform model refers to a fundamental assumption guiding the analysis of functional Magnetic Resonance Imaging ( ... Specifically, the model holds that the fMRI signal is approximately proportional to a measure of local neural activity, ...
*  Generalized linear mixed model
Generalized Linear Mixed Models, CRC Press Jiang, J. (2007), Linear and Generalized Linear Mixed Models and Their Applications ... a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor ... They also inherit from GLMs the idea of extending linear mixed models to non-normal data. GLMMs provide a broad range of models ... to fit GLMM models. Generalized estimating equation Hierarchical generalized linear model Breslow, N. E.; Clayton, D. G. (1993 ...
*  Standard linear solid model
The standard linear solid (SLS) model, also known as the Zener model, is a method of modeling the behavior of a viscoelastic ... Often, the simpler Maxwell model and the Kelvin-Voigt model are used. These models often prove insufficient, however; the ... The standard linear solid model combines aspects of the Maxwell and Kelvin-Voigt models to accurately describe the overall ... SLS is the simplest model that predicts both phenomena. Materials undergoing strain are often modeled with mechanical ...
*  Non-linear sigma model
Sigma model Chiral model Little Higgs Skyrmion, a soliton in non-linear sigma models WZW model Fubini-Study metric, a metric ... The non-linear σ-model was introduced by Gell-Mann & Lévy (1960, section 6), who named it after a field corresponding to a ... A nonlinear σ model with G/H as the target manifold with a G-invariant Riemannian metric and a zero potential is called a ... These are modelled as R^2 with a point at infinity, and hence have the same topology as the O(3) instantons in 1+1 dimensions. ...
*  Linear no-threshold model
The linear no-threshold model (LNT) is a model used in radiation protection to quantify radiation exposure and set regulatory ... The linear-no-threshold model was first expressed by John Gofman, and rejected by the Department of Energy, according to Gofman ... The linear no-threshold model is used to extrapolate the expected number of extra deaths caused by exposure to environmental ... A linear model has long been used in health physics to set maximum acceptable radiation exposures. The United States-based ...
*  Standard linear solid Q model for attenuation and dispersion
Ursin and Toverud compared different Q models including the above model (SLS-model). In order to compare the different models ... A standard linear solid Q model (SLS) for attenuation and dispersion is one of many mathematical Q models that gives a ... This model was compared with the Kolsky-Futterman model. The Kolsky-Futterman model was first described in the article ' ... The standard linear solid model is developed from the stress-strain relation. Using a linear combination of springs and ...
*  Linear-nonlinear-Poisson cascade model
The linear-nonlinear-Poisson (LNP) cascade model is a simplified functional model of neural spike responses. It has been ... There are three stages of the LNP cascade model. The first stage consists of a linear filter, or linear receptive field, which ... If the nonlinearity f {\displaystyle f} is a fixed invertible function, then the LNP model is a generalized linear model. In ... the linear stage of the LNP model can be generalized to a bank of linear filters, and the nonlinearity becomes a function of ...
*  Regression toward the mean
In other words, if linear regression is the appropriate model for a set of data points whose sample correlation coefficient is ... such a line that minimizes the sum of squared residuals of the linear regression model. In other words, numbers α and β solve ... Linear models". Modern Mathematical Statistics. John Wiley & Sons. ISBN 978-0-471-81472-6. Francis Galton (1886). "Regression ... He quantified this trend, and in doing so invented linear regression analysis, thus laying the groundwork for much of modern ...
*  Rosemary A. Bailey
She has written books on the design of experiments, on association schemes, and on linear models in statistics. She is ... Bailey, R. A. (1994). Normal linear models. London: External Advisory Service, University of London. ISBN 0-7187-1176-9. Bailey ... ISBN 978-0-521-68357-9. Speed, T. P.; Bailey, R. A. (1987). "Factorial Dispersion Models". International Statistical Review / ...
*  Houdini (software)
Houdini covers all the major areas of 3D production, including these: Modeling - All standard geometry entities including ... it enables and encourages non-linear development; and new operators can be created in terms of existing operators, a flexible ... a technique which attempts to more accurately model the interactions of light. Derivative Inc. is a spin-off of Side Effects ... for procedural modelling. POPs - particle operators - used to manipulate particles systems. CHOPs - channel operators - for ...
*  John Nelder
In statistical theory, Nelder and Wedderburn proposed the generalized linear model. Generalized linear models were formulated ... "Generalized Linear Models", J. R. Statist. Soc. A, 135 (1972) 370-384. McCullagh, P. and J.A. Nelder. 1989. Generalized Linear ... "for their monograph Generalized Linear Models (1983)". As tribute on his eightieth birthday, a festschrift Methods and Models ... Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood. Chapman & Hall/CRC, Boca Raton, Florida. ISBN ...