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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
Method for estimating the unknown parameters in a linear regression model
statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model by the principle
Ordinary_least_squares
Concept in regression analysis mathematics
as the least-angle regression algorithm. An important difference between lasso regression and Tikhonov regularization is that lasso regression forces
Regularized_least_squares
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 modeling method
partial least squares regression is the extension of the PCR method which does not suffer from the mentioned deficiency. Least-angle regression is an estimation
Linear_regression
Statistical method
least squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression;
Partial least squares regression
Partial_least_squares_regression
Approximation method in statistics
such as the least angle regression algorithm. One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty
Least_squares
Statistical technique
generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation
Total_least_squares
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)
Least squares approximation of linear functions to data
linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares
Linear_least_squares
Topics referred to by the same term
Stars), a rap group Launch and recovery system (diving) Least-angle regression, a regression algorithm for high-dimensional data Lesotho Amateur Radio
LARS
Approximation method in statistics
regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x , θ i ) = θ 1 +
Non-linear_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
Method for model fitting in statistics
Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge
Weighted_least_squares
Statistical estimation technique
In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there
Generalized_least_squares
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
Method of statistical factor analysis
In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic
Stepwise_regression
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 optimality criterion
<1} , one obtains quantile regression. The case of τ = 1 / 2 {\displaystyle \tau =1/2} gives the standard regression by least absolute deviations and is
Least_absolute_deviations
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
American statistician
articles including "Bootstrap", "Least-Angle Regression and LASSO for Large Datasets", and "Least Angle and L1 Penalized Regression: A Review". "Tim Hesterberg"
Tim_Hesterberg
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
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 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
bias Least absolute deviations Least-angle regression Least squares Least-squares spectral analysis Least squares support vector machine Least trimmed
List_of_statistics_articles
throughput omics data. Regression: least squares, ridge regression, least angle regression, elastic net, kernel ridge regression, support vector machines
Mlpy
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
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
Overview of and topical guide to machine learning
(SOM) Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS)
Outline_of_machine_learning
Method for solving certain optimization problems
Robust Regression, Course Notes, University of Minnesota. Numerical Methods for Least Squares Problems by Åke Björck (Chapter 4: Generalized Least Squares
Iteratively reweighted least squares
Iteratively_reweighted_least_squares
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
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
Measure of linear correlation
correlation coefficient and the angle φ between the two regression lines, y = gX(x) and x = gY(y), obtained by regressing y on x and x on y respectively
Pearson correlation coefficient
Pearson_correlation_coefficient
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 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 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 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
Statistical method
Multiple linear regression, also known as multivariable linear regression, is a generalization of simple linear regression to the case of more than one
Multiple_linear_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
Process of constructing a curve that has the best fit to a series of data points
Biological Data Using Linear and Nonlinear Regression. By Harvey Motulsky, Arthur Christopoulos. Regression Analysis By Rudolf J. Freund, William J. Wilson
Curve_fitting
Measure of prediction accuracy of a forecast
regression problems and in model evaluation, because of its very intuitive interpretation in terms of relative error. Consider a standard regression setting
Mean absolute percentage error
Mean_absolute_percentage_error
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
Analysis (KPCA) K-Means Clustering Least-Angle Regression (LARS/LASSO) Linear Regression Bayesian Linear Regression Local Coordinate Coding Locality-Sensitive
Mlpack
American statistician
ISBN 9781107149892. Clinical trials Empirical Bayesian Fisher, Ronald Least-angle regression Fisher information Hinkley, David V. Likelihood function Observed
Bradley_Efron
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
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
Statistical estimation method
In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output
Binary_regression
Sequence of data points over time
simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial
Time_series
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
Statistical model
}}_{FE}} is then obtained by an OLS regression of y ¨ {\displaystyle {\ddot {y}}} on X ¨ {\displaystyle {\ddot {X}}} . At least three alternatives to the within
Fixed_effects_model
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
Type of inference
ordinary differential equation, boolean network, or Linear regression models, e.g. Least-angle regression, by Bayesian network or based on Information theory
Biological_network_inference
Shape in mathematics
where is a "phase change" in the data, by fitting two lines using linear regression. Elbow method Maximum power point tracking Terrell, John Alan (1913).
Knee_of_a_curve
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
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
Similarity measure for number sequences
defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided
Cosine_similarity
Statistical regression where the dependent variable can take only two values
In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word
Probit_model
Type of statistical model
can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became
Multilevel_model
best fit line for the Zisman Plot. To find the best fit line a least squares regression is recommended by using a computer program such as Microsoft Excel
Zisman_Plot
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
Regression analysis technique
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is
Binomial_regression
Method of simultaneous inference
regression models. One of the first developments in simultaneous inference, it was devised by Working and Hotelling for the simple linear regression model
Working–Hotelling_procedure
Type of average of a collection of numbers
such as phases or angles. Taking the arithmetic mean of 1° and 359° yields a result of 180°. This is incorrect for two reasons: Angle measurements are
Arithmetic_mean
Constrained least squares problem
mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed
Non-negative_least_squares
Performance metric in football and hockey
logistic regression identified five factors that had a significant effect on determining the success of a kicked shot: distance from the goal; angle from
Expected_goals
Plot using the dispersal of scattered dots to show the relationship between variables
For a linear correlation, the best-fit procedure is known as linear regression and is guaranteed to generate a correct solution in a finite time. No
Scatter_plot
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 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
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
Concept in probability theory and statistics
for including other right-side variables in a multiple regression; but while multiple regression gives unbiased results for the effect size, it does not
Partial_correlation
Correlation of a signal with a time-shifted copy of itself, as a function of shift
least squares (OLS), the adequacy of a model specification can be checked in part by establishing whether there is autocorrelation of the regression residuals
Autocorrelation
Free and open-source statistical program
analyses for regression, classification and clustering: Regression Boosting Regression Decision Tree Regression K-Nearest Neighbors Regression Neural Network
JASP
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
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
Periodicity computation method
of progressively determined frequencies using a standard linear regression or least-squares fit. The frequencies are chosen using a method similar to
Least-squares spectral analysis
Least-squares_spectral_analysis
Type of metric geometry
distance or L1 distance (see Lp space). This geometry has been used in regression analysis since the 18th century, and is often referred to as LASSO. Its
Taxicab_geometry
Statistical method
be sampled and variables fixed. Factor regression model is a combinatorial model of factor model and regression model; or alternatively, it can be viewed
Factor_analysis
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
Way of inferring information from cross-covariance matrices
interpreted as regression coefficients linking X C C A {\displaystyle X^{CCA}} and Y C C A {\displaystyle Y^{CCA}} and may also be negative. The regression view
Canonical_correlation
Inverse of the average of the inverses of a set of numbers
triangle with legs a and b and altitude h from the hypotenuse to the right angle, h2 is half the harmonic mean of a2 and b2. Let t and s (t > s) be the sides
Harmonic_mean
Circular statistical graph of proportionality
a pie chart, the arc length of each slice (and consequently its central angle and area) is proportional to the quantity it represents. While it is named
Pie_chart
Subspace of n-space whose dimension is (n-1)
vision and natural language processing. In multiple linear regression with more than two regressors, the datapoint and its predicted value via a linear model
Hyperplane
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
Distinction between nominal, ordinal, interval and ratio variables
3.398. Mosteller, Frederick; Tukey, John W. (1977). Data analysis and regression: a second course in statistics. Reading, Mass: Addison-Wesley Pub. Co
Level_of_measurement
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
Unit of information
collecting, classifying, and analyzing data using five possible angles of analysis (at least three) to maximize the research's objectivity and permit an understanding
Data
Concept in statistics
the most important statistical regression models: the linear model, Poisson regression for counts, and logistic regression for binary responses. However
Vector generalized linear model
Vector_generalized_linear_model
N-th root of the product of n numbers
For all positive data sets containing at least one pair of unequal values, the harmonic mean is always the least of the three means, while the arithmetic
Geometric_mean
Generalization of the one-dimensional normal distribution to higher dimensions
in the distribution of the vector of residuals in the ordinary least squares regression. The X i {\displaystyle X_{i}} are in general not independent;
Multivariate normal distribution
Multivariate_normal_distribution
Mathematical model of the physical space
serves as a linearized design matrix in statistical regression and curve fitting; see non-linear least squares. The Jacobian is also used in random matrices
Euclidean_geometry
Mathematical term
two lines are perpendicular. In statistics, the gradient of the least-squares regression best-fitting line for a given sample of data may be written as:
Slope
Relationship between two lines that meet at a right angle
geometric objects are perpendicular if they intersect at right angles, i.e. at an angle of 90 degrees or π/2 radians. The condition of perpendicularity
Perpendicular
Variable representing a random phenomenon
with all parts of the range being "equally likely". In this case, X = the angle spun. Any real number has probability zero of being selected, but a positive
Random_variable
Matrix of partial derivatives of a vector-valued function
serves as a linearized design matrix in statistical regression and curve fitting; see non-linear least squares. The Jacobian is also used in random matrices
Jacobian matrix and determinant
Jacobian_matrix_and_determinant
Class of extinct, Paleozoic arthropods
visual surface varies at least as strongly as it does in the rear, but the lack of a clear reference point similar to the genal angle makes it difficult to
Trilobite
Method of logical reasoning
"all rectangles so far examined have four right angles, so the next one I see will have four right angles." This would treat logical relations as something
Inductive_reasoning
Type of chart
represented on axes starting from the same point. The relative position and angle of the axes is typically uninformative, but various heuristics, such as
Radar_chart
Statistical model
Part of a series on Regression analysis Models Linear regression Simple regression Polynomial regression General linear model Generalized linear model
Random_effects_model
Class of statistical models
Mixed model Fixed effects model Generalized linear mixed model Linear regression Mixed-design analysis of variance Multilevel model Random effects model
Nonlinear_mixed-effects_model
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