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Statistical estimation method
a single value, as in linear regression. Binary regression is usually analyzed as a special case of binomial regression, with a single outcome ( n = 1
Binary_regression
Statistical model for a binary dependent variable
combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model
Logistic_regression
Regression analysis technique
variables. Binomial regression is closely related to binary regression: a binary regression can be considered a binomial regression with n = 1 {\displaystyle
Binomial_regression
Regression for more than two discrete outcomes
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than
Multinomial logistic regression
Multinomial_logistic_regression
Dividing things between two categories
statistical technique to effect the classification is binary regression. When measuring the accuracy of a binary classifier, the simplest way is to count the errors
Binary_classification
Data whose unit can take on only two possible states
the grouped data). Regression analysis on predicted outcomes that are binary variables is known as binary regression; when binary data is converted to
Binary_data
Regression analysis for modeling ordinal data
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e.
Ordinal_regression
Regularization technique for ill-posed problems
Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models
Ridge_regression
Set of statistical processes for estimating the relationships among variables
called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which
Regression_analysis
Statistical linear model
model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is
General_linear_model
Statistical modeling technique
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional
Quantile_regression
Statistical method
squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of
Partial least squares regression
Partial_least_squares_regression
Least squares approximation of linear functions to data
^{\mathsf {T}}\mathbf {y} .} Optimal instruments regression is an extension of classical IV regression to the situation where E[εi | zi] = 0. Total least
Linear_least_squares
Statistics concept
regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,
Regression_validation
Statistics concept
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable
Polynomial_regression
Statistical modeling method
regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression
Linear_regression
Method for estimating the unknown parameters in a linear regression model
especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent
Ordinary_least_squares
Moving average and polynomial regression method for smoothing data
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its
Local_regression
Statistical regression technique
multilevel regression with poststratification model involves the following pair of steps: MRP step 1 (multilevel regression): The multilevel regression model
Multilevel regression with poststratification
Multilevel_regression_with_poststratification
Concept in statistical mathematics
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable
Segmented_regression
Mathematical model for stochastic processes
Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included, are
Generalized functional linear model
Generalized_functional_linear_model
Choice between two or more discrete alternatives
customer decides to purchase. Techniques such as logistic regression and probit regression can be used for empirical analysis of discrete choice. Discrete
Discrete_choice
Method for model fitting in statistics
(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance
Weighted_least_squares
Method for solving certain optimization problems
maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers
Iteratively reweighted least squares
Iteratively_reweighted_least_squares
Form of modelling that uses statistics to predict outcomes
probability of a certain event occurring (e.g. Binary regression), or a scalar response variable (e.g. Linear regression) The usage of predictive modelling in
Predictive_modelling
Statistical regression where the dependent variable can take only two values
procedure, such an estimation being called a probit regression. Suppose a response variable Y is binary, that is it can have only two possible outcomes which
Probit_model
Information-theoretic measure
cross-entropy loss for logistic regression is equal to the gradient of the squared-error loss for linear regression (up to a constant factor). To see
Cross-entropy
Statistical model for count data
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes
Poisson_regression
Statistics model
statistics, a linear probability model (LPM) is a special case of a binary regression model. Here the dependent variable for each observation takes values
Linear_probability_model
Specialized form of regression analysis, in statistics
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship
Robust_regression
Statistical technique
used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the
Principal component regression
Principal_component_regression
Mathematical functions
functions used. The generalization of the binary hyperbolastic regression to multinomial hyperbolastic regression has a response variable y i {\displaystyle
Hyperbolastic_functions
Type of numerical analysis
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations
Isotonic_regression
Regression model for ordinal dependent variables
logit model or proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal dependent variables—first
Ordered_logit
Regression algorithm
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron
Least-angle_regression
American statistician
data visualization,[A] equivalences between binary regression and survival analysis,[B] and robust regression.[C] Gasko completed her Ph.D. in statistics
Miriam_Gasko_Donoho
Numeric stand-ins in regression analysis
In regression analysis, a dummy variable (also known as indicator variable or just dummy) is one that takes a binary value (0 or 1) to indicate the absence
Dummy_variable_(statistics)
Type of statistical model
the regression model would be to add an additional independent categorical variable to account for the location (i.e. a set of additional binary predictors
Multilevel_model
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
Statistical model containing both fixed effects and random effects
Mixed models are often preferred over traditional analysis of variance regression models because they don't rely on the independent observations assumption
Mixed_model
Regression models that combine parametric and nonparametric models
In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations
Semiparametric_regression
Concept in regression analysis mathematics
least-angle regression algorithm. An important difference between lasso regression and Tikhonov regularization is that lasso regression forces more entries
Regularized_least_squares
Regression analysis
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination
Nonlinear_regression
Regression models accounting for possible errors in independent variables
error model is a regression model that accounts for measurement errors in the independent variables. In contrast, standard regression models assume that
Errors-in-variables_model
Theorem related to ordinary least squares
of the Regression Model". Econometric Theory. Oxford: Blackwell. pp. 17–36. ISBN 0-631-17837-6. Goldberger, Arthur (1991). "Classical Regression". A Course
Gauss–Markov_theorem
Method of statistical analysis
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
Bayesian_linear_regression
In statistics, unit-weighted regression is a simplified and robust version (Wainer & Thissen, 1976) of multiple regression analysis where only the intercept
Unit-weighted_regression
Statistical regression method
particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2
Elastic_net_regularization
Constrained least squares problem
linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial logistic regression Mixed logit Probit Multinomial probit
Non-negative_least_squares
Software bug in which features stop working
change. Regressions are often caused by encompassed bug fixes included in software patches. One approach to avoiding this kind of problem is regression testing
Software_regression
Visualization method for regularization
linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial logistic regression Mixed logit Probit Multinomial probit
L-curve
Statistical technique
taken into account. It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models
Total_least_squares
Statistical tool used in meta-analyses
Meta-regression is a meta-analysis that uses regression analysis to combine, compare, and synthesize research findings from multiple studies while adjusting
Meta-regression
Statistical model
including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a
Fixed_effects_model
Statistical optimality criterion
the idea of least absolute deviations regression is just as straightforward as that of least squares regression, the least absolute deviations line is
Least_absolute_deviations
Approximation method in statistics
as the least angle regression algorithm. One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty is
Least_squares
Empirical law on the variance of species in a habitat
error of the regression, α and β are the constant and slope of the regression respectively, sβ2 is the variance of the slope of the regression, N is the
Taylor's_law
Category of regression analysis
Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information
Nonparametric_regression
Statistics concept
distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead
Errors_and_residuals
Statistical model
_{ij}+U_{i}+W_{ij},\,} where S e x i j {\displaystyle \mathrm {Sex} _{ij}} is a binary dummy variable and P a r e n t s E d u c i j {\displaystyle \mathrm {ParentsEduc}
Random_effects_model
Statistical estimation technique
parameters in a linear regression model. It is used when there is a non-zero amount of correlation between the residuals in the regression model. GLS is employed
Generalized_least_squares
Kind of ratio
regression better fitting values at the ends of the domain. It is also reflected in the influence functions of various data points on the regression coefficients:
Studentized_residual
Machine learning problem
is that which has the highest probability. Binary probabilistic classifiers are also called binary regression models in statistics. In econometrics, probabilistic
Probabilistic_classification
Statistical technique
Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching. Its main field of application
Conditional logistic regression
Conditional_logistic_regression
Probability distribution
procedures, including Bayesian modeling of the directional data, Bayesian binary regression, and Bayesian graphical modeling. In Bayesian analysis, new distributions
Modified half-normal distribution
Modified_half-normal_distribution
Metric for fit of statistical models
Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness
Goodness_of_fit
Class of statistical models
(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the
Generalized_linear_model
Statistical model
linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial logistic regression Mixed logit Probit Multinomial probit
Mixed_logit
Approximation method in statistics
the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x ,
Non-linear_least_squares
Categorization of data using statistics
algorithms include Logistic regression – Statistical model for a binary dependent variable Multinomial logistic regression – Regression for more than two discrete
Statistical_classification
Machine learning algorithm
continuous values (typically real numbers) are called regression trees. More generally, the concept of regression tree can be extended to any kind of object equipped
Decision_tree_learning
Branch of statistics mathematics
are three special cases of functional nonlinear regression models. Functional polynomial regression models may be viewed as a natural extension of the
Functional_data_analysis
Method of multiple regression analysis used in behavioural genetics
genetics, DeFries–Fulker (DF) regression, also sometimes called DeFries–Fulker extremes analysis, is a type of multiple regression analysis designed for estimating
DeFries–Fulker_regression
Least-Angle Regression (LARS/LASSO) Linear Regression Bayesian Linear Regression Local Coordinate Coding Locality-Sensitive Hashing (LSH) Logistic regression Max-Kernel
Mlpack
Statistical method
linear regression models. This simple case reveals a substantial amount about the estimator. These include its relationship to ridge regression and best
Lasso_(statistics)
Bayesian approach to multivariate linear regression
Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is
Bayesian multivariate linear regression
Bayesian_multivariate_linear_regression
Statistical rule of thumb
from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk
One_in_ten_rule
Approximate interference technique in Bayesian networks
(2011). "Non-conjugate Variational Message Passing for Multinomial and Binary Regression" (PDF). NeurIPS. Infer.NET: an inference framework which includes
Variational_message_passing
Statistical method
parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y
Regression discontinuity design
Regression_discontinuity_design
Set of methods for supervised statistical learning
predictive performance than other linear models, such as logistic regression and linear regression. Classifying data is a common task in machine learning. Suppose
Support_vector_machine
Generalized method of moments estimator in econometrics
variables estimation. In the Arellano–Bond method, first difference of the regression equation are taken to eliminate the individual effects. Then, deeper lags
Arellano–Bond_estimator
Statistical methods to correct for endogeneity problems
the exponential regression framework, which the following discussion follows closely. While the example focuses on a Poisson regression model, it is possible
Control function (econometrics)
Control_function_(econometrics)
Function in statistics
{(2x-1)^{2n+1}}{2n+1}}.} Several approaches have been explored to adapt linear regression methods to a domain where the output is a probability value ( 0 , 1
Logit
confused with the multivariate probit model, which is used to model correlated binary outcomes for more than one independent variable. It is assumed that we have
Multinomial_probit
Statistician
of moving with Taqqu, she remained at Cornell and began working on binary regression using computer simulations. She completed her doctorate at Cornell
Claudia_Czado
Software engineering
introduced a specific regression was described as "source change isolation" in 1997 by Brian Ness and Viet Ngo of Cray Research. Regression testing was performed
Bisection (software engineering)
Bisection_(software_engineering)
generalization of the probit model used to estimate several correlated binary outcomes jointly. For example, if it is believed that the decisions of sending
Multivariate_probit_model
Periodicity computation method
sinusoids of progressively determined frequencies using a standard linear regression or least-squares fit. The frequencies are chosen using a method similar
Least-squares spectral analysis
Least-squares_spectral_analysis
Statistical model
characterized either as mixed models, or in a hierarchical form, or a multilevel regression with poststratification. The resulting estimates for each area (subgroup)
Fay–Herriot_model
Class of statistical survival models
itself be described as a regression model. There is a relationship between proportional hazards models and Poisson regression models which is sometimes
Proportional_hazards_model
Statistical property
which performs an auxiliary regression of the squared residuals on the independent variables. From this auxiliary regression, the explained sum of squares
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Measure of ordinal association
methods. It is also used as a quality measure of binary choice or ordinal regression (e.g., logistic regressions) and credit scoring models. We say that two
Somers'_D
Israeli–American economist
models, models for distribution effects, and quantile regression with an endogenous binary regressor. Angrist has also explored the link between local average
Joshua_Angrist
Ambiguous term in statistics
approach, see Bennett (2002) Isotonic regression, see Zadrozny and Elkan (2002) Platt scaling (a form of logistic regression), see Lewis and Gale (1994) and
Calibration_(statistics)
Smooth function in statistics
linear model framework and a tool used in non-parametric regression, semiparametric regression and functional data analysis. In parametric modeling, variance
Variance_function
Indicator for how well data points fit a line or curve
remaining 51% of the variability is still unaccounted for. For regression models, the regression sum of squares, also called the explained sum of squares,
Coefficient_of_determination
Diagnostic plot of binary classifier ability
Notable proposals for regression problems are the so-called regression error characteristic (REC) Curves and the Regression ROC (RROC) curves. In the
Receiver operating characteristic
Receiver_operating_characteristic
Machine learning practice of supervised learning
sets of data items. Quantification is also different from regression, since in regression the training data items have real-valued labels, while in quantification
Quantification (machine learning)
Quantification_(machine_learning)
Variable capable of taking on a limited number of possible values
distribution (the Bernoulli distribution) and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable"
Categorical_variable
Patched software backdoor
Andres Freund reported the backdoor after investigating a performance regression in a development version of Debian, a Linux distribution. Freund noticed
XZ_Utils_backdoor
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