regional regression models of annual streamflow for the united states
... the hydrologic cycle, a special effort was made to obtain estimates of climate characteristics that are as accurate as possible using the current state of the art. Table 1 summarizes the climate characteristics computed for this study. Spatially weighted estimates of each climate characteristic in T ...
... the hydrologic cycle, a special effort was made to obtain estimates of climate characteristics that are as accurate as possible using the current state of the art. Table 1 summarizes the climate characteristics computed for this study. Spatially weighted estimates of each climate characteristic in T ...
Atmospheric oscillations do not explain the temperature
... make sure that outliers are not driving the conclusions. Observations were removed if the corresponding diagonal element of the OLS hat matrix exceeded twice the mean of the diagonal elements (Kmenta 1986, pp. 424-426). This resulted in removal of 21 observations, leaving a sample size of 419. The c ...
... make sure that outliers are not driving the conclusions. Observations were removed if the corresponding diagonal element of the OLS hat matrix exceeded twice the mean of the diagonal elements (Kmenta 1986, pp. 424-426). This resulted in removal of 21 observations, leaving a sample size of 419. The c ...
GLM (Generalized Linear Model) #1 (version 9)
... Use of the canonical link simplifies the maximum likelihood analysis (by which we find estimates of the b’s). If you consider the idea of the sequence that translates Xi b into θi as described in section 2.2, notice that a very significant simplifying benefit is obtained by the canonical link. But t ...
... Use of the canonical link simplifies the maximum likelihood analysis (by which we find estimates of the b’s). If you consider the idea of the sequence that translates Xi b into θi as described in section 2.2, notice that a very significant simplifying benefit is obtained by the canonical link. But t ...
Discrete Dependent Variable Models
... The maximum of L is solved by differentiating the function with respect to each of the beta’s and setting the partial derivatives equal to zero, or the values of β1, ……, βK that provides the maximum of L . In many cases the log likelihood function is globally concave, so that if a solution to the fi ...
... The maximum of L is solved by differentiating the function with respect to each of the beta’s and setting the partial derivatives equal to zero, or the values of β1, ……, βK that provides the maximum of L . In many cases the log likelihood function is globally concave, so that if a solution to the fi ...
Learning Transformation Models for Ranking and Survival Analysis
... ’margin’ is for Support Vector Machines for classification. The use of this model structure in the context of high dimensional data, as well as for estimating non-linear, and additive models based on primal-dual kernel machines, and for sparse models is indicated. Given n observations, the present m ...
... ’margin’ is for Support Vector Machines for classification. The use of this model structure in the context of high dimensional data, as well as for estimating non-linear, and additive models based on primal-dual kernel machines, and for sparse models is indicated. Given n observations, the present m ...
Ecological opportunity and sexual selection together
... ©2012 Macmillan Publishers Limited. All rights reserved ...
... ©2012 Macmillan Publishers Limited. All rights reserved ...
Macro-Integration-06-M-Chow
... data. In the past few decades, different methods for disaggregating low frequency data to high frequency data have been developed. These methods can be classified into two types of approaches. ...
... data. In the past few decades, different methods for disaggregating low frequency data to high frequency data have been developed. These methods can be classified into two types of approaches. ...
american english significance
... dollars of equal “purchasing power”), and this was used to homogenize the figures from national sources. With that we could also calculate per-capita income levels for the different countries and areas of those countries, which are expressed as GDP per capita figures (GDPpc) and appear in the last ...
... dollars of equal “purchasing power”), and this was used to homogenize the figures from national sources. With that we could also calculate per-capita income levels for the different countries and areas of those countries, which are expressed as GDP per capita figures (GDPpc) and appear in the last ...
Poisson Regression - Department of Statistics
... Poisson regression is a form of a generalized linear model where the response variable is modeled as having a Poisson distribution. The Poisson distribution models random variables with non-negative integer values. For large means, the Poisson distribution is well approximated by the normal distribu ...
... Poisson regression is a form of a generalized linear model where the response variable is modeled as having a Poisson distribution. The Poisson distribution models random variables with non-negative integer values. For large means, the Poisson distribution is well approximated by the normal distribu ...
Squared Multiple Correlation and Squared Cross
... The violation of multivariate normality considered here is one that occurs frequently in psychology. In particular, the dependent variable is not assumed to be continuous but, instead, is discrete. Three examples illustrate the categorical nature of important dependent variables in psychology. The f ...
... The violation of multivariate normality considered here is one that occurs frequently in psychology. In particular, the dependent variable is not assumed to be continuous but, instead, is discrete. Three examples illustrate the categorical nature of important dependent variables in psychology. The f ...
Extended abstract - Conference
... models are often used to analyse such hierarchical data, or to estimate small domains means. Hierarchical sample data may be selected with unequal probabilities from a clustered and stratified population. The sample design is informative when the selection probabilities are associated with the study ...
... models are often used to analyse such hierarchical data, or to estimate small domains means. Hierarchical sample data may be selected with unequal probabilities from a clustered and stratified population. The sample design is informative when the selection probabilities are associated with the study ...
Linear regression
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.)In linear regression, data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis.Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.Linear regression has many practical uses. Most applications fall into one of the following two broad categories: If the goal is prediction, or forecasting, or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y. Given a variable y and a number of variables X1, ..., Xp that may be related to y, linear regression analysis can be applied to quantify the strength of the relationship between y and the Xj, to assess which Xj may have no relationship with y at all, and to identify which subsets of the Xj contain redundant information about y.Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the ""lack of fit"" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares loss function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms ""least squares"" and ""linear model"" are closely linked, they are not synonymous.