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A generalized splitwindow algorithm for retrieving landsurface temperature
 IEEE Transactions on Geoscience and Remote Sensing
, 1996
"... Abstract We propose a generalized splitwindow method for retrieving landsurface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the splitwindow algorithm for LST must vary with the viewing angle, if we are to achieve a LST accura ..."
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Cited by 103 (8 self)
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accuracy of about 1 K for the whole scan swath range (1t5.5 " from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. We obtain these coefficients from regression analysis of radiative transfer simulations, and we analyze
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks
"... Abstract. Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for ..."
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Abstract. Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach
Logistic Regression Analysis of Operational Errors and Routine Operations Using Sector Characteristics
, 2009
"... This document is disseminated under the sponsorship ..."
Whom You Know Matters: Venture Capital Networks and Investment Performance,
 Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm'slength, spotmarket transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
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Cited by 138 (8 self)
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. Network Analysis Methodology The aim of network analysis is to describe the structure of networks, by focusing first and foremost on the relationships that exist among a set of economic actors and less on the individual actors' characteristics (such as age, wealth, etc.). For instance, a network
Longerterm effects of Head Start
 American Economic Review
, 2002
"... Abstract Public early intervention programs like Head Start are often justified as investments in children. Yet nothing is known about the longterm effects of Head Start. This paper draws on unique data from the Panel Study of Income Dynamics to provide new evidence on the effects of Head Start on ..."
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Cited by 131 (5 self)
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value from the sample and included a dummy variable in the regression which indicated that a value had been assigned. 10 Despite the richness of the PSID, there may well be other unmeasured characteristics that distinguish Head Start children from their peers and which cannot be controlled
Nonparametric “regression” when errors are positioned at endpoints
, 2009
"... Increasing practical interest has been shown in regression problems where the errors, or disturbances, are centred in a way that reflects particular characteristics of the mechanism that generated the data. In economics this occurs in problems involving data on markets, productivity and auctions, wh ..."
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Cited by 4 (0 self)
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Increasing practical interest has been shown in regression problems where the errors, or disturbances, are centred in a way that reflects particular characteristics of the mechanism that generated the data. In economics this occurs in problems involving data on markets, productivity and auctions
Ridge Regression in Practice Ridge Regression in Practice*
"... JSTOR is a notforprofit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about J ..."
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" [9] to his procedure because of the similarity of its mathematics to methods he used earlier [8], i.e., "ridge analysis," for graphically depicting the characteristics of second order response surface equations in many predictor variables. Key properties applicable to ridge regression are E
Bootstrap Confidence Intervals for Regression Error Characteristic Curves Evaluating the Prediction Error of Software Cost Estimation Models
"... The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several algorithmic and artificial intelligence models and methods, appeared so far in the literature. Despite the several comparison studies, there seems to be a d ..."
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technique, namely the bootstrap method in order to evaluate the standard error and bias of the accuracy measures, whereas bootstrap confidence intervals are constructed for the Regression Error Characteristic curves. The tool can be applied to any cost estimation situation in order to study the behavior
Regression models for highdimensional data with correlated errors
"... Given a generalised linear regression model: y = Xβ + ε (1) where y is the n × 1 response vector; X is an n × p model matrix representing the predictors; and β is a p × 1 vector of coefficients to estimate. For mathematical simplicity, it is typical to set the first predictor as the intercept β0 so ..."
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Given a generalised linear regression model: y = Xβ + ε (1) where y is the n × 1 response vector; X is an n × p model matrix representing the predictors; and β is a p × 1 vector of coefficients to estimate. For mathematical simplicity, it is typical to set the first predictor as the intercept β0 so
Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values
, 2001
"... Estimating the mean and the covariance matrix of an incomplete dataset and filling in missing values with imputed values is generally a nonlinear problem, which must be solved iteratively. The expectation maximization (EM) algorithm for Gaussian data, an iterative method both for the estimation of m ..."
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Cited by 109 (4 self)
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, in which the number of variables typically exceeds the sample size. The regularized EM algorithm is based on iterated analyses of linear regressions of variables with missing values on variables with available values, with regression coefficients estimated by ridge regression, a regularized regression
Results 11  20
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1,694