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...

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,}...

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...

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 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...

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

slowly: (binary) iterated logarithm (log*) is less than 5 for all practical data (265536 bits); (binary) log-log (log log n) is less than 6 for virtually all...

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

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...

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

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

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

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

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

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

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

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;...

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...

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

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...

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