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Regression With Time Series Structure
Regression or regressions may refer to: Arts and entertainment * ''Regression'' (film), a 2015 horror film by Alejandro Amenábar, starring Ethan Hawke and Emma Watson * ''Regression'' (magazine), an Australian punk rock fanzine (1982–1984) * ''Regressions'' (album), 2010 album by Cleric Computing * Software regression, the appearance of a bug in functionality that was working correctly in a previous revision ** Regression testing, a software testing method which seeks to uncover regression bugs Hypnosis * Age regression in therapy, a process claiming to retrieve memories * Past life regression, a process claiming to retrieve memories of previous lives Science * Marine regression, coastal advance due to falling sea level, the opposite of marine transgression * Regression (medicine), a characteristic of diseases to express lighter symptoms or less extent (mainly for tumors), without disappearing totally * Regression (psychology), a defensive reaction to some unaccepte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regression (film)
''Regression'' is a 2015 psychological thriller mystery film, mystery film directed and written by Alejandro Amenábar. The film stars Ethan Hawke and Emma Watson, with David Thewlis, Lothaire Bluteau, Dale Dickey, David Dencik, Peter MacNeill, Devon Bostick, and Aaron Ashmore in supporting roles. The film had its world premiere at the San Sebastián International Film Festival on September 18, 2015. It was released in the United States on October 9, 2015, by The Weinstein Company under the banner RADiUS-TWC. The film received mostly negative reviews from critics. Plot In Hoyer, Minnesota, in 1990, Detective Bruce Kenner investigates the case of John Gray, who admits to sexually abusing his 17-year-old daughter Angela but has no recollection of the abuse. They seek the help of Professor Kenneth Raines to use recovered-memory therapy on John Gray to retrieve his memories, and come to suspect that their colleague Detective George Nesbitt is involved. They detain him but fail to fin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regression Toward The Mean
In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where if one sample of a random variable is extreme, the next sampling of the same random variable is likely to be closer to its mean. Furthermore, when many random variables are sampled and the most extreme results are intentionally picked out, it refers to the fact that (in many cases) a second sampling of these picked-out variables will result in "less extreme" results, closer to the initial mean of all of the variables. Mathematically, the strength of this "regression" effect is dependent on whether or not all of the random variables are drawn from the same distribution, or if there are genuine differences in the underlying distributions for each random variable. In the first case, the "regression" effect is statistically likely to occur, but in the second case, it may occur less strongly or not at all. Regression toward the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stepwise Regression
In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually, this takes the form of a forward, backward, or combined sequence of ''F''-tests or ''t''-tests. The frequent practice of fitting the final selected model followed by reporting estimates and confidence intervals without adjusting them to take the model building process into account has led to calls to stop using stepwise model building altogetherFlom, P. L. and Cassell, D. L. (2007) "Stopping stepwise: Why stepwise and similar selection methods are bad, and what you should use," NESUG 2007. or to at least make sure model uncertainty is correctly reflected by using prespecified, automatic criteria together with more complex standard error estimates that remain unbiased. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robust Regression
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations). Robust regression methods are designed to limit the effect that violations of assumptions by the underlying data-generating process have on regression estimates. For example, least squares estimates for regression models are highly sensitive to outliers: an outlier with twice the error magnitude of a typical observation contributes four (two squared) times as much to the squared error loss, and therefore has more leverage over the regression estimates. The Huber loss function is a robust alternative to standard square error loss that r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nonparametric Regression
Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information derived from the data. That is, no parametric equation is assumed for the relationship between predictors and dependent variable. A larger sample size is needed to build a nonparametric model having a level of uncertainty as a parametric model because the data must supply both the model structure and the parameter estimates. Definition Nonparametric regression assumes the following relationship, given the random variables X and Y: : \mathbb \mid X=x= m(x), where m(x) is some deterministic function. Linear regression is a restricted case of nonparametric regression where m(x) is assumed to be a linear function of the data. Sometimes a slightly stronger assumption of additive noise is used: : Y = m(X) + U, where the random variable U is the `noise term', with mean 0. Without the assumption that m belongs to a specific ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nonlinear Regression
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations (iterations). General In nonlinear regression, a statistical model of the form, \mathbf \sim f(\mathbf, \boldsymbol\beta) relates a vector of independent variables, \mathbf, and its associated observed dependent variables, \mathbf. The function f is nonlinear in the components of the vector of parameters \beta, but otherwise arbitrary. For example, the Michaelis–Menten model for enzyme kinetics has two parameters and one independent variable, related by f by: f(x,\boldsymbol\beta)= \frac This function, which is a rectangular hyperbola, is because it cannot be expressed as a linear combination of the two \betas. Systematic error may be present in the independent variables but its ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Logistic Regression
In statistics, a logistic model (or logit model) is a statistical model that models the logit, log-odds of an event as a linear function (calculus), linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimation theory, estimates the parameters of a logistic model (the coefficients in the linear or non linear combinations). In binary logistic regression there is a single binary variable, binary dependent variable, coded by an indicator variable, where the two values are labeled "0" and "1", while the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Simple Linear Regression
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the ''x'' and ''y'' coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variable. The adjective ''simple'' refers to the fact that the outcome variable is related to a single predictor. It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared '' residual'' (vertical distance between the point of the data set and the fitted line), and the goal is to make the sum of these squared deviations as small as possible. In this case, the slope of the fitted line is equal to the corre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Regression
In statistics, linear regression is a statistical model, model that estimates the relationship between a Scalar (mathematics), scalar response (dependent variable) and one or more explanatory variables (regressor or independent variable). A model with exactly one explanatory variable is a ''simple linear regression''; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression, which predicts multiple correlated dependent variables rather than a single dependent variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimation theory, estimated from the data. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nodal Regression
Nodal precession is the precession of the orbital plane of a satellite around the rotational axis of an astronomical body such as Earth. This precession is due to the non-spherical nature of a rotating body, which creates a non-uniform gravitational field. The following discussion relates to low Earth orbit of artificial satellites, which have no measurable effect on the motion of Earth. The nodal precession of more massive, natural satellites like the Moon is more complex. Around a spherical body, an orbital plane would remain fixed in space around the gravitational primary body. However, most bodies rotate, which causes an equatorial bulge. This bulge creates a gravitational effect that causes orbits to precess around the rotational axis of the primary body. The direction of precession is opposite the direction of revolution. For a typical prograde orbit around Earth (that is, in the direction of primary body's rotation), the longitude of the ascending node decreases, that is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regression (magazine)
Australian musicians played and recorded some of the earliest punk rock, led by The Saints who released their first single in 1976. Subgenres of punk music, such as local hardcore acts, still have a strong cult following throughout Australia. Many of the pioneers, like The Saints, Sydney band Radio Birdman, and young Perth musician Kim Salmon, were highly influenced by proto-punk sounds from Detroit. A distinct Brisbane punk scene emerged in the 1970s. By 1977, other bands began to form in Sydney, under the influence of Radio Birdman and other local and overseas acts. During the late 1970s, former members of Radio Birdman contributed to several new bands. These bands and other Australian and overseas punk acts were supported by public radio stations. In Melbourne scene, art rock had segued into punk, then evolved into post-punk, typified by the careers of Nick Cave, Rowland S. Howard and the little band scene. Another pioneering figure of Australian postpunk was Saints ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
