Results 261 to 270 of about 2,379,546 (312)
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International Statistical Review / Revue Internationale de Statistique, 1992
Summary: The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. We pose alternative ways to view this model.
Ingram Olkin, Shlomo Yitzhaki
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Summary: The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. We pose alternative ways to view this model.
Ingram Olkin, Shlomo Yitzhaki
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Regression-Discontinuity Analysis
2008The regression discontinuity (RD) data design is a quasi-experimental evaluation design first introduced by Thistlethwaite and Campbell (1960) as an alternative approach to evaluating social programmes. The design is characterized by a treatment assignment or selection rule which involves the use of a known cut-off point with respect to a continuous ...
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Regression and Correlation Analysis
1987Correlation is a tool for understanding the relationship between two quantities. Regression considers how one quantity is influenced by another. In correlation analysis the two quantities are considered symmetrically: in regression analysis one is supposed dependent on the other, in an unsymmetric way. Extensions to sets of quantities are important.
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Regression Analysis and Estimating Regression Models
2020A forecast is merely a prediction about the future values of data. Financial forecasts span a broad range of areas, and each of the forecasts is of interest to a number of people and departments in a firm. A sales manager may wish to forecast sales (either in units sold or revenues generated).
Mustafa Gultekin +2 more
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Canadian Journal of Statistics, 1981
AbstractThe paper describes two regression models—principal components and maximum‐likelihood factor analysis—which may be used when the stochastic predictor varibles are highly intereorrelated and/or contain measurement error. The two problems can occur jointly, for example in social‐survey data where the true (but unobserved) covariance matrix can be
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AbstractThe paper describes two regression models—principal components and maximum‐likelihood factor analysis—which may be used when the stochastic predictor varibles are highly intereorrelated and/or contain measurement error. The two problems can occur jointly, for example in social‐survey data where the true (but unobserved) covariance matrix can be
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Regression Analysis by Example.
Journal of the American Statistical Association, 1979B. Price +2 more
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2008
Abstract : In regression analysis, the goal is to determine the values of parameters for a function to best fit a set of data observations. Put another way, regression attempts to best describe what inputs result in a given output. Though there are many complex forms of regression models, the simplest is a linear regression model.
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Abstract : In regression analysis, the goal is to determine the values of parameters for a function to best fit a set of data observations. Put another way, regression attempts to best describe what inputs result in a given output. Though there are many complex forms of regression models, the simplest is a linear regression model.
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The Analysis of Disturbances in Regression Analysis
Journal of the American Statistical Association, 1965Classical regression analysis is concerned with the estimation of the parameter vector\(beta \)of the equation\(y = X\beta + u\)(1.1)where y is the T-element column vector of values taken by the dependent variable, X the \(TxA\) matrix of values taken by the A independent variables, \(beta \) a column of A parameters, and u a column of T disturbance.
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Metabolomics in cancer research and emerging applications in clinical oncology
Ca-A Cancer Journal for Clinicians, 2021Daniel R Schmidt +2 more
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