Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for...

A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model...

median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the...

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization...

model that models the log-odds of an event as a linear combination of one or more independent variables. In regression analysis, logistic regression (or...

The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits...

k log ⁡ x + log ⁡ a . {\displaystyle \log y=k\log x+\log a.} Setting X = log ⁡ x {\displaystyle X=\log x} and Y = log ⁡ y , {\displaystyle Y=\log y,}...

log(μ) be a linear model. This produces the "cloglog" transformation log ⁡ ( − log ⁡ ( 1 − p ) ) = log ⁡ ( μ ) . {\displaystyle \log(-\log(1-p))=\log(\mu...

theorem Hopfield network Interacting particle system Ising model Log-linear analysis Markov chain Markov logic network Maximum entropy method Stochastic...

results. Common methods include Linear Analysis, Semi-log Analysis and Log-Log Analysis The Linear Method is the analysis of the function N(r), where N...

In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other...

In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression...

Local regression Log-Cauchy distribution Log-Laplace distribution Log-normal distribution Log-linear analysis Log-linear model Log-linear modeling – redirects...

The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models...

pp. 1855–1863. Petitjean F, Webb GI, Nicholson AE (2013). Scaling log-linear analysis to high-dimensional data (PDF). International Conference on Data...

extended to survival estimation. The DeepSurv model proposes to replace the log-linear parameterization of the CoxPH model with a multi-layer perceptron. Further...

standard HyperLogLog estimator E {\textstyle E} above. Otherwise, use Linear Counting: E ⋆ = m log ⁡ ( m V ) {\textstyle E^{\star }=m\log \left({\frac...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

Feature selection Freedman's paradox Grid search Identifiability Analysis Log-linear analysis Model identification Occam's razor Optimal design Parameter identification...

study of the relative efficiency of regression analysis, discriminant analysis, and log linear analysis in predicting the future enrollment status of educational...

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...

in log probability. Similar to multiplication operations in linear-scale becoming simple additions in log-scale, an addition operation in linear-scale...

analogous function which is linear on ⁠ x {\displaystyle x} ⁠ for the normal curve 'probit'." — Joseph Berkson (1944) Log odds was used extensively by...

Explained variation Linear trend estimation Mixed-design analysis of variance Multivariate analysis of covariance (MANCOVA) Permutational analysis of variance...

areas such as regression analysis, curve fitting and data modeling. The least squares method can be categorized into linear and nonlinear forms, depending...

quasilinear time (also referred to as log-linear time) if T ( n ) = O ( n log k ⁡ n ) {\displaystyle T(n)=O(n\log ^{k}n)} for some positive constant k;...

function. Popular loss functions include the hinge loss (for linear SVMs) and the log loss (for linear logistic regression). If the regularization function R...

Laird and Donald Olivier (1981). "Covariance Analysis of Censored Survival Data Using Log-Linear Analysis Techniques". Journal of the American Statistical...

(A. veronensis has relatively more robust head). Differences in log-linear analysis of both lizard's colouration were negligible, the same goes for insufficient...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...