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Reduced Major Axis Regression
In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational errors on both dependent and independent variables are taken into account. It is a 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 of the data is generically equivalent to the best, in the Frobenius norm, low-rank approximation of the data matrix. Linear model Background In the least squares method of data modeling, the objective function to be minimized, ''S'', is a quadratic form: :S=\mathbf, where ''r'' is the vector of residuals and ''W'' is a weighting matrix. In linear least squares the model contains equations which are linear in the parameters appearing in the parameter vector \boldsymbol\beta, so the residuals are given by :\mathbf. There are ''m'' observations in y and ''n'' parameters in β with ' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Total Least Squares
In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational errors on both dependent and independent variables are taken into account. It is a 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 of the data is generically equivalent to the best, in the Frobenius norm, low-rank approximation of the data matrix. Linear model Background In the least squares method of data modeling, the objective function to be minimized, ''S'', is a quadratic form: :S=\mathbf, where ''r'' is the vector of residuals and ''W'' is a weighting matrix. In linear least squares the model contains equations which are linear in the parameters appearing in the parameter vector \boldsymbol\beta, so the residuals are given by :\mathbf. There are ''m'' observations in y and ''n'' parameters in β wi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
The Johns Hopkins University Press
Johns Hopkins University Press (also referred to as JHU Press or JHUP) is the publishing division of Johns Hopkins University. It was founded in 1878 and is the oldest continuously running university press in the United States. The press publishes books and journals, and operates other divisions including fulfillment and electronic databases. Its headquarters are in Charles Village section of Baltimore, Maryland. In 2017, after the retirement of Kathleen Keane, who is credited with modernizing JHU Press for the digital age, the university appointed new director Barbara Pope. Overview Daniel Coit Gilman, the first president of Johns Hopkins University, inaugurated the press in 1878. The press began as the university's Publication Agency, publishing the ''American Journal of Mathematics'' in its first year and the '' American Chemical Journal'' in its second. It published its first book, ''Sidney Lanier: A Memorial Tribute'', in 1881 to honor the poet who was one of the univers ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Errors-in-variables Model
In statistics, an errors-in-variables model or a measurement error model is a regression model that accounts for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples. For simple linear regression the effect is an underestimate of the coefficient, known as the '' attenuation bias''. In non-linear models the direction of the bias is likely to be more complicated. Motivating example Consider a simple linear regression model of the form : y_ = \alpha + \beta x_^ + \varepsilon_t\,, \quad t=1,\ldots,T, where x_^ denotes the ''true'' bu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regression Dilution
Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the underestimation of its absolute value), caused by errors in the independent variable. Consider fitting a straight line for the relationship of an outcome variable ''y'' to a predictor variable ''x'', and estimating the slope of the line. Statistical variability, measurement error or random noise in the ''y'' variable causes uncertainty in the estimated slope, but not bias: on average, the procedure calculates the right slope. However, variability, measurement error or random noise in the ''x'' variable causes bias in the estimated slope (as well as imprecision). The greater the variance in the ''x'' measurement, the closer the estimated slope must approach zero instead of the true value. It may seem counter-intuitive that noise in the predictor variable ''x'' induces a bias, but noise in the outcome variable ''y'' does not. Recall that linear regressi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Paul Samuelson
Paul Anthony Samuelson (May 15, 1915 – December 13, 2009) was an American economist who was the first American to win the Nobel Memorial Prize in Economic Sciences. When awarding the prize in 1970, the Swedish Royal Academies stated that he "has done more than any other contemporary economist to raise the level of scientific analysis in economic theory". "In a career that spanned seven decades, he transformed his field, influenced millions of students and turned MIT into an economics powerhouse" Samuelson was one of the most influential economists of the latter half of the 20th century."Paul Samuelson: The last of the great general economists died on December 13th, aged 94" ''The Economist'', December 17, 2009 In 1996, he was awarded the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scale Invariance
In physics, mathematics and statistics, scale invariance is a feature of objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor, and thus represent a universality. The technical term for this transformation is a dilatation (also known as dilation). Dilatations can form part of a larger conformal symmetry. *In mathematics, scale invariance usually refers to an invariance of individual functions or curves. A closely related concept is self-similarity, where a function or curve is invariant under a discrete subset of the dilations. It is also possible for the probability distributions of random processes to display this kind of scale invariance or self-similarity. *In classical field theory, scale invariance most commonly applies to the invariance of a whole theory under dilatations. Such theories typically describe classical physical processes with no characteristic length scale. *In quantum field theory, scale inva ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Analysis Of Variance
Analysis of variance (ANOVA) is a family of statistical methods used to compare the Mean, means of two or more groups by analyzing variance. Specifically, ANOVA compares the amount of variation ''between'' the group means to the amount of variation ''within'' each group. If the between-group variation is substantially larger than the within-group variation, it suggests that the group means are likely different. This comparison is done using an F-test. The underlying principle of ANOVA is based on the law of total variance, which states that the total variance in a dataset can be broken down into components attributable to different sources. In the case of ANOVA, these sources are the variation between groups and the variation within groups. ANOVA was developed by the statistician Ronald Fisher. In its simplest form, it provides a statistical test of whether two or more population means are equal, and therefore generalizes the Student's t-test#Independent two-sample t-test, ''t''- ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Maximum-likelihood
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference. If the likelihood function is differentiable, the derivative test for finding maxima can be applied. In some cases, the first-order conditions of the likelihood function can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when the random errors are assumed to have normal distributions with the same variance. From the perspective of Bayesian inference, MLE ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mahalanobis Distance
The Mahalanobis distance is a distance measure, measure of the distance between a point P and a probability distribution D, introduced by Prasanta Chandra Mahalanobis, P. C. Mahalanobis in 1936. The mathematical details of Mahalanobis distance first appeared in the ''Journal of The Asiatic Society of Bengal'' in 1936. Mahalanobis's definition was prompted by the problem of similarity measure, identifying the similarities of skulls based on measurements (the earliest work related to similarities of skulls are from 1922 and another later work is from 1927). Raj Chandra Bose, R.C. Bose later obtained the sampling distribution of Mahalanobis distance, under the assumption of equal dispersion. It is a multivariate generalization of the square of the standard score z=(x- \mu)/\sigma: how many standard deviations away P is from the mean of D. This distance is zero for P at the mean of D and grows as P moves away from the mean along each principal component axis. If each of these axes ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Distance From A Point To A Line
The distance (or perpendicular distance) from a point to a line is the shortest Euclidean distance, distance from a fixed Point (geometry), point to any point on a fixed infinite Line (mathematics), line in Euclidean geometry. It is the length of the line segment which joins the point to the line and is perpendicular to the line. The formula for calculating it can be derived and expressed in several ways. Knowing the shortest distance from a point to a line can be useful in various situations—for example, finding the shortest distance to reach a road, quantifying the scatter on a graph, etc. In Deming regression, a type of linear curve fitting, if the dependent and independent variables have equal variance this results in orthogonal regression in which the degree of imperfection of the fit is measured for each data point as the perpendicular distance of the point from the regression line. Cartesian coordinates Line defined by an equation In the case of a line in the plane giv ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jacobian Matrix And Determinant
In vector calculus, the Jacobian matrix (, ) of a vector-valued function of several variables is the matrix of all its first-order partial derivatives. If this matrix is square, that is, if the number of variables equals the number of components of function values, then its determinant is called the Jacobian determinant. Both the matrix and (if applicable) the determinant are often referred to simply as the Jacobian. They are named after Carl Gustav Jacob Jacobi. The Jacobian matrix is the natural generalization to vector valued functions of several variables of the derivative and the differential of a usual function. This generalization includes generalizations of the inverse function theorem and the implicit function theorem, where the non-nullity of the derivative is replaced by the non-nullity of the Jacobian determinant, and the multiplicative inverse of the derivative is replaced by the inverse of the Jacobian matrix. The Jacobian determinant is fundamentally use ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Non-linear Least Squares
Non-linear least squares is the form of least squares analysis used to fit a set of ''m'' observations with a model that is non-linear in ''n'' unknown parameters (''m'' ≥ ''n''). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations. There are many similarities to linear least squares, but also some significant differences. In economic theory, the non-linear least squares method is applied in (i) the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors (m(x,\theta_i) = \theta_1 + \theta_2 x^). Theory Consider a set of m data points, (x_1, y_1), (x_2, y_2), \dots, (x_m, y_m), and a curve (model function) \hat = f(x, \boldsymbol \beta), that in addition to the variable x also depends on n parameters, \boldsymbol \beta = (\beta_1, \beta_2, \dots, \beta_n), with m\ge ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
