Search Java Classes and Packages
#Com.numericalmethod.suanshu.stats.regression Classes and Interfaces - 70 results found.
| Name | Description | Type | Package | Framework |
| BackwardElimination | Constructs a GLM model for a set of observations using the backward elimination method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| BackwardElimination .Step | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu | |
| ConstrainedLASSObyLARS | This class solves the constrained form of LASSO by modified least angle regression (LARS) and linear interpolation: | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
| ConstrainedLASSOProblem | A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
| EliminationByAIC | In each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| EliminationByZValue | In each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| FixedEffectsModel | Fits the panel data to this linear model: y_{it} = alpha_{i}+X_{it}mathbf{eta}+u_{it} | Class | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
| ForwardSelection | Constructs a GLM model for a set of observations using the forward selection method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| ForwardSelection .Step | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu | |
| GeneralizedLinearModel | The Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
| GeneralizedLinearModelQuasiFamily | GLM for the quasi-families. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
| GLMBeta | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu | |
| GLMBinomial | This is the Binomial distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMExponentialDistribution | This interface represents a probability distribution from the exponential family. | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMFamily | Family provides a convenient way to specify the error distribution and link function used in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMFitting | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu | |
| GLMGamma | This is the Gamma distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMGaussian | This is the Gaussian distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMInverseGaussian | This is the Inverse Gaussian distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMModelSelection | Given a set of observations {y, X}, we would like to construct a GLM to explain the data. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| GLMModelSelection .ModelNotFound | Throw a ModelNotFound exception when fail to construct a model toSee Also:Serialized Form | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| GLMPoisson | This is the Poisson distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
| GLMProblem | This is a Generalized Linear regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
| GLMResiduals | Residual analysis of the results of a Generalized Linear Model regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
| IWLS | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu | |
| LARSFitting | This class computes the entire LARS sequence of coefficients and fits, starting from zero to theSee Also:B. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso.lars | SuanShu |
| LARSFitting .Estimators | Gets the estimated sequence of A. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso.lars | SuanShu |
| LARSProblem | Least Angle Regression (LARS) is a regression algorithm for high-dimensional data. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso.lars | SuanShu |
| LinearModel | A linear model provides fitting and the residual analysis (goodness of fit). | Interface | com.numericalmethod.suanshu.stats.regression.linear | SuanShu |
| LinkCloglog | This class represents the complementary log-log link function: g(x) = log(-log(1 - x)) | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkFunction | This interface represents a link function g(x) in Generalized Linear Model (GLM). | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkIdentity | This class represents the identity link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkInverse | This class represents the inverse link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkInverseSquared | This class represents the inverse-squared link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkLog | This class represents the log link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkLogit | This class represents the logit link function: g(x) = log(frac{mu}{1-mu}) | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkProbit | This class represents the Probit link function, which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LinkSqrt | This class represents the square-root link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
| LMBeta | Beta coefficients are the outcomes of fitting a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear | SuanShu |
| LMDiagnostics | This class collects some diagnostics measures for the goodness of fit based on the residulas for a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.residualanalysis | SuanShu |
| LMInformationCriteria | The information criteria measure the goodness of fit of an estimated statistical model. | Class | com.numericalmethod.suanshu.stats.regression.linear.residualanalysis | SuanShu |
| LMProblem | This is a linear regression or a linear model (LM) problem. | Class | com.numericalmethod.suanshu.stats.regression.linear | SuanShu |
| LMResiduals | This is the residual analysis of the results of a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.residualanalysis | SuanShu |
| LogisticBeta | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu | |
| LogisticProblem | A logistic regression problem is a variation of the OLS regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
| LogisticRegression | A logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
| LogisticResiduals | Residual analysis of the results of a logistic regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
| OLSBeta | Class | com.numericalmethod.suanshu.stats.regression.linear.ols | SuanShu | |
| OLSRegression | (Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.ols | SuanShu |
| OLSResiduals | This is the residual analysis of the results of an ordinary linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.ols | SuanShu |
| PanelData | A panel data refers to multi-dimensional data frequently involving measurements over time. | Class | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
| PanelData .Transformation | Transforms the data, e. | Interface | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
| PanelRegression | Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics, which deals with two-dimensional (cross sectional/times series) panel data. | Interface | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
| QuasiBinomial | This is the quasi Binomial distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiDistribution | This interface represents the quasi-distribution used in GLM. | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiFamily | This interface represents the quasi-family used in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiGamma | This is the quasi Gamma distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiGaussian | This is the quasi Gaussian distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiGLMBeta | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu | |
| QuasiGLMNewtonRaphson | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu | |
| QuasiGLMProblem | This class represents a quasi generalized linear regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
| QuasiGLMResiduals | Residual analysis of the results of a quasi Generalized Linear Model regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
| QuasiInverseGaussian | This is the quasi Inverse-Gaussian distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| QuasiPoisson | This is the quasi Poisson distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
| SelectionByAIC | In each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| SelectionByZValue | In each step, the most significant factor is added, until all remaining factors are insignificant. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
| UnconstrainedLASSObyCoordinateDescent | This class solves the unconstrained form of LASSO, that is, min_w left { left | Xw - y ight |_2^2 + lambda * left | w | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
| UnconstrainedLASSObyQP | This class solves the unconstrained form of LASSO (i. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
| UnconstrainedLASSOProblem | A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
| WeightedRSS | Weighted sum of squared residuals (RSS) for a given function (f(. | Class | com.numericalmethod.suanshu.stats.regression | SuanShu |