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What is Science Slides 91
What is Science Slides 91

IBM SPSS Modeler 17 Algorithms Guide i
IBM SPSS Modeler 17 Algorithms Guide i

The Gender Pay Gap in Europe
The Gender Pay Gap in Europe

Long-Run Covariability
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... simplification offered by these averages is that they are normally distributed in large samples even though the stochastic process generating the data may exhibit substantial persistence (Müller and Watson (forthcoming)). This allows large-sample inference about covariability parameters to be transfo ...


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... maturity segment, may lead to more e¢cient and more accurate forecasts of GDP. In an OLS framework, since yields of di¤erent maturities are highly cross-correlated, it may be di¢cult to use multiple yields as regressors because of collinearity problems. This collinearity suggests that we may be able ...
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... regression-based hedge, a result that is very different from Kroner and Sultan. For cross hedges (not studied by Kroner and Sultan), we obtain the same conclusion except for pairs of currencies that are so weakly unrelated that no practitioner would use them as hedges of each other. Lastly, given th ...
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STAT - Statistics

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The Excess Bond Returns and Debt Maturity

... this issue, a cross-sectional OLS regression method is utilized in a combination with the time-series mean of each variable for an individual firm. However, this method only accounts for the variation across firms without analyzing the time-variation (Barclay and Smith, 1995). In order to investigat ...
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Edge Effects in Small Forest Fragments

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Cointegration tests - Rose

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loss given default modelling for mortgage loans

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An Alignment-Free Regression Approach for Estimating Allele

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Spatial Dependence in Apartment Offering Prices

... magnitudes of the estimates and their significance but may also lead to serious errors in the interpretation of standard regression diagnostics such as for heteroscedasticity. Comparing the baseline model with a model which explicitly corrects for spatial endogenously gives us insights into spatial ...
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A Guide to Using EViews with Using Econometrics

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individuals and moving range charts in NCSS

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Pivotal Estimation via Square-root Lasso in Nonparametric

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Statistical Methods (201112)

Chapter 3 - Faculty Personal Web Pages
Chapter 3 - Faculty Personal Web Pages

ORACLE INEQUALITIES FOR HIGH DIMENSIONAL
ORACLE INEQUALITIES FOR HIGH DIMENSIONAL

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Regression analysis

In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable; for example, correlation does not imply causation.Many techniques for carrying out regression analysis have been developed. Familiar methods such as linear regression and ordinary least squares regression are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite-dimensional.The performance of regression analysis methods in practice depends on the form of the data generating process, and how it relates to the regression approach being used. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a sufficient quantity of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small effects or questions of causality based on observational data, regression methods can give misleading results.In a narrower sense, regression may refer specifically to the estimation of continuous response variables, as opposed to the discrete response variables used in classification. The case of a continuous output variable may be more specifically referred to as metric regression to distinguish it from related problems.
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