Kernel ridge regression - Kernel ridge regression combines squared-error regression with a penalty on function complexity in a kernel-defined feature space.
Kitagawa - Oaxaca - Blinder decomposition - Kitagawa - Oaxaca - Blinder decomposition is a family of methods for expressing a difference between groups as components associated with their composition...
Klecka's tau - Klecka's tau is a descriptive measure of how much a classification rule reduces errors relative to a specified random-assignment baseline.
Knockoffs (statistics) - Knockoffs are synthetic comparison variables used in statistical procedures for selecting relevant predictors while controlling the false discovery rate...
Kolmogorov-Smirnov test - The Kolmogorov - Smirnov test is a distribution-comparison test based on the largest vertical difference between cumulative distribution functions.
Kriging - Kriging is a family of prediction methods that use a modeled covariance or variogram to combine observed values when estimating an unobserved value, often...
KruskalWallis one-way analysis of variance - The Kruskal - Wallis one-way analysis of variance is a rank-based test for comparing independent groups on an ordered or numerical outcome.
Kuiper's test - Kuiper's test is a goodness-of-fit or two-sample distribution-comparison test based on the sum of the largest upward and downward differences between...
Kurtosis - Kurtosis is a standardized fourth-moment measure of a distribution.
Lack of fit - Lack of fit is systematic disagreement between a model's assumed relationship and the pattern supported by the data.
Lack-of-fit sum of squares - The lack-of-fit sum of squares is the part of regression residual variation attributable to discrepancies between observed means at replicated predictor...
Lag operator - The lag operator is a compact notation for shifting a time series backward in time.
Land use regression model - A land use regression model is an empirical model that relates measurements at monitoring locations to geographic predictors, then uses those relationships...
Latin hypercube design - A Latin hypercube design is a sampling arrangement that spreads a chosen number of runs across every input variable's range or probability distribution.
Latin square - A Latin square is an n-by-n array filled with n distinct symbols so that each symbol appears exactly once in every row and exactly once in every column.
Latin square design - A Latin square design is an experimental arrangement that compares n treatments while blocking two nuisance factors, each having n levels.
LDL, lower detection limit - LDL, or lower detection limit, describes a measurement method's ability to distinguish a low-level analyte or signal from background under stated conditions.
Least absolute deviation regression - Least absolute deviation regression, often called least absolute deviations or LAD regression, estimates a model by minimizing the sum of absolute residuals...
Least Angle Regression - Least Angle Regression, usually abbreviated LARS, is an algorithm that builds a piecewise-linear path of linear regression fits by progressively...
Least squares estimate - A least squares estimate is a parameter value obtained by minimizing a sum of squared discrepancies between observed data and a specified model.
Least squares regression - Least squares regression fits a relationship between predictors and a response by minimizing the sum of squared response residuals.
Least-squares support vector machine - A least-squares support vector machine, or LS-SVM, is a kernel-based learning formulation that uses squared errors and equality constraints in place of the...
Lepage test - The Lepage test is a two-sample rank test that combines evidence about location and scale differences.
Leptokurtic - Leptokurtic describes a distribution whose kurtosis exceeds that of a normal distribution: Pearson kurtosis greater than 3, or excess kurtosis greater than...
Levene test - The Levene test assesses whether two or more groups have equal variances by comparing their absolute deviations from a group-specific center.
Leverage - Leverage is a regression diagnostic that measures how unusual an observation's predictor configuration is relative to the fitted design and how strongly its...
Lewandowski-Kurowicka-Joe distribution - The Lewandowski-Kurowicka-Joe distribution, usually called the LKJ distribution, is a probability distribution over positive-definite correlation matrices.
Life table - A life table summarizes survival and mortality across successive ages or time intervals.
Likelihood - Likelihood is the support a statistical model assigns to the observed data as its parameters or specified hypotheses vary.
Likelihood function - A likelihood function is the joint probability mass or density of observed data, treated as a function of unknown model parameters while the data are held...
Likelihood ratio chi- square - Likelihood ratio chi-square is a statistic that compares the maximum likelihood under a restricted model with the maximum likelihood under a larger, nested...
Likelihood weighting - Likelihood weighting is an importance-sampling method for approximate inference in a Bayesian network.
Likert scale - A Likert scale is a measurement approach that combines responses to multiple statements intended to assess an attitude or related construct, commonly using...
Limited dependent variable - A limited dependent variable is an outcome whose possible or observed values are restricted in a way that matters for statistical modeling.
Lindley's paradox - Lindley's paradox is a disagreement that can arise between a conventional significance test and a Bayesian comparison of a point-null hypothesis with an...
Line sampling - Line sampling is a geographical sampling approach in which selected lines determine where observations are made or which objects are included.
Linear model - A linear model combines specified features through a weighted sum, usually with an intercept.
Linear prediction - Linear prediction estimates an unknown quantity using a weighted sum of available observations or features, often with an intercept.
Linear regression - Linear regression models a numerical outcome using a sum of explanatory features multiplied by coefficients, usually with an intercept.
Linear relationship - A linear relationship is one whose systematic pattern can be represented by a straight line on the stated measurement scales.
Linear transformation - In the supplied statistics glossary, a linear transformation changes a numerical variable by multiplying it by a constant and adding a constant: Y = a + bX.
Linear trend estimation - Linear trend estimation fits a straight-line change over time to observed data, commonly using a model such as Yt = a + bt + error.
Linearity of regression - Linearity of regression is the assumption that the systematic mean relationship is adequately represented by the specified linear form.
Linkage disequilibrium score regression - Linkage disequilibrium score regression, usually abbreviated LD Score regression or LDSC, is a method that relates genome-wide association statistics to...
List sample - A list sample is a sample selected from an explicit list of units in the population being studied. The list acts as the sampling frame.
Local model - In the supplied Aalto dictionary's federated-learning terminology, a local model is the hypothesis space assigned to a particular node of a learning network.
Local regression - Local regression estimates a response curve or surface by fitting simple regressions to neighborhoods of observations.
Locally weighted regression - Locally weighted regression predicts a response by fitting a regression that gives greater influence to observations near the query point.