6,052 articles
Moving average
A moving average, also known as a rolling or running average, is a statistic that averages successive subsets of a data series, smoothing short-term fluctuations in statistics, signal processing, and finance.
Moving horizon estimation
Moving horizon estimation (MHE), also called receding-horizon estimation, estimates a dynamic system's state by solving a constrained optimization over a sliding window of recent measurements, handling nonlinear models and constraints that defeat the Kalman filter.
Moving least squares
Moving least squares is a local regression method that fits a weighted polynomial over moving neighborhoods of scattered data, used in statistics and surface reconstruction.
Moving mesh method
A moving mesh method is a numerical technique for solving partial differential equations that continuously relocates a fixed number of mesh nodes toward regions where the solution varies rapidly.
Moving particle simulation
Moving particle simulation, the moving particle semi-implicit (MPS) method, is a meshfree Lagrangian particle method for incompressible fluid flows, introduced by S. Koshizuka and Y. Oka at the University of Tokyo in 1996.
Moving sofa problem
The moving sofa problem, or moving couch problem, asks for the largest-area planar shape that can move around a right-angled corner in a unit-width hallway.
Moving-average model
A moving-average model is a time series model expressing the current observation as a linear combination of current and past white-noise shocks, used for forecasting.
Mplus
Mplus is a commercial statistical program for latent variable modeling, developed by Muthén & Muthén of Los Angeles and first released in November 1998. It is widely used in the social, behavioral, and health sciences.
mpmath
mpmath is a free, BSD-licensed Python library for arbitrary-precision real and complex floating-point arithmetic, created by Fredrik Johansson in 2007 and serving as the numerical evaluation engine for SymPy.
Multi-factor model
A multi-factor model is a statistical model explaining asset returns as a linear function of several common factors, used in finance for pricing and risk analysis.
Multi-group analysis
Multi-group analysis (MGA) is a structural equation modeling technique that estimates a model separately for predefined subgroups and tests whether path coefficients, loadings, or means differ across groups.
Multi-objective linear programming
Multi-objective linear programming (MOLP) is an optimization method that solves linear programs with several objective functions at once, replacing optimality with efficiency and finding nondominated points.
Multi-state model
A multi-state model is a statistical model in which individuals move among a finite number of states, used to analyze disease progression in survival analysis.
Multicollinearity
Multicollinearity is a condition in regression analysis in which two or more predictor variables are highly intercorrelated, inflating the variances of estimated coefficients and making them numerically unstable.
Multifactor dimensionality reduction
Multifactor dimensionality reduction (MDR) is a nonparametric, model-free data-mining method that detects gene-gene and gene-environment interactions in case-control genetic studies, introduced by Ritchie and colleagues in 2001.
Multifractal detrended fluctuation analysis
Multifractal detrended fluctuation analysis (MF-DFA) is a statistical method that measures multifractality in nonstationary time series, reported by Jan W. Kantelhardt and colleagues in 2002.
Multigrid method
The multigrid method is an iterative solver and preconditioner for linear systems, especially elliptic PDEs, that corrects fine-grid solutions on coarser grids to solve N unknowns in O(N) work.
Multigroup analysis (statistics)
Multigroup analysis (MGA) is a structural equation modeling method, introduced by Jöreskog in 1971, that fits one model to several groups and tests whether parameters such as loadings and means differ across them.
Multilevel logistic regression
Multilevel logistic regression is a regression method for binary outcomes that adds normally distributed random effects at several levels, such as patients within hospitals, to model clustering.
Multilevel meta-analysis
Multilevel meta-analysis is a statistical method combining effect sizes from multiple studies while modeling their nested structure, such as several effect sizes from the same study, using hierarchical random-effects models.
Multilevel model
A multilevel model, also called hierarchical linear modeling, is a regression model for nested data, such as pupils within schools, estimating effects at each level.
Multilevel Monte Carlo
Multilevel Monte Carlo (MLMC) is a variance reduction technique, introduced by Michael Giles in 2008, that estimates expectations by simulation through a telescoping sum of corrections across levels of increasing accuracy.
Multilevel regression
Multilevel regression, also called hierarchical linear modeling, is a statistical method for fitting regression models to nested or clustered data such as students within schools.
Multilevel regression and poststratification
Multilevel regression and poststratification (MRP, sometimes Mister P) is a statistical method introduced by Gelman and Little in 1997 that estimates state-level opinion from a single national poll.
Multilinear algebra
Multilinear algebra is the branch of algebra dealing with multilinear maps between vector spaces, whose central objects are tensors; it underlies tensor calculus, differential forms, and tensor decompositions used in data analysis.
Multimodal distribution
A multimodal distribution, or multimodal frequency distribution, is a probability distribution whose density has more than one mode, often signaling a mixture of subpopulations; the two-peak case is bimodal.
Multimodal logic
Multimodal logic is a modal logic with several modal operators, each with its own accessibility relation, built from unimodal components by combination methods such as fusion and product.
Multinomial distribution
The multinomial distribution is a discrete probability distribution giving the probability of counts across k mutually exclusive categories after n independent trials, each with fixed category probabilities.
Multinomial logistic regression
Multinomial logistic regression, also known as the multinomial logit or maximum entropy (MaxEnt) classifier, is a regression model for categorical outcomes with more than two unordered categories.
Multinomial model
The multinomial model is the standard probability model for counts of categorical outcomes: over n independent trials, each falls into one of k categories with fixed probabilities summing to one.