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Does teacher preparation matter? Evidence about teacher certification, Teach for America, and teacher effectiveness. Education Policy Analysis, 13(42). Retrieved from http://epaa.asu.edu/epaa/v13n42
, 2005
"... Readers are free to copy, display, and distribute this article, as long as the work is attributed to the author(s) and Education Policy Analysis Archives, it is distributed for noncommercial purposes only, and no alteration or transformation is made in the work. More details of this Creative Common ..."
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Readers are free to copy, display, and distribute this article, as long as the work is attributed to the author(s) and Education Policy Analysis Archives, it is distributed for noncommercial purposes only, and no alteration or transformation is made in the work. More details of this Creative Commons license are available at
Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model
 The American Statistician
, 2000
"... In the presence of heteroscedasticity, OLS estimates are unbiased, but the usual tests of significance are generally inappropriate and their use can lead to incorrect inferences. Tests based on a heteroscedasticity consistent covariance matrix (HCCM), however, are consistent even in the presence o ..."
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Cited by 111 (0 self)
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In the presence of heteroscedasticity, OLS estimates are unbiased, but the usual tests of significance are generally inappropriate and their use can lead to incorrect inferences. Tests based on a heteroscedasticity consistent covariance matrix (HCCM), however, are consistent even in the presence of heteroscedasticity of an unknown form. Most applications that use a HCCM appear to rely on the asymptotic version known as HC0. Our Monte Carlo simulations show that HC0 often results in incorrect inferences when N ≤ 250, while three relatively unknown, small sample versions of the HCCM, and especially a version known as HC3, work well even for N ’s as small as 25. We recommend that: 1) data analysts should correct for heteroscedasticity using a HCCM whenever there is reason to suspect heteroscedasticity; 2) the decision to use a HCCMbased tests should not be determined by a screening test for heteroscedasticity; and 3) when N ≤ 250, the HCCM known as HC3 should be used. Since HC3 is simple to compute, we encourage authors of statistical software to add this estimator to their programs. 1
2002: A spatiotemporal approach for global validation and analysis of MODIS aerosol products. Geophys
 Res. Lett
, 2003
"... new data sets of the global distribution and properties of aerosol are being retrieved, and need to be validated and analyzed. A system has been put in place to generate spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of the ..."
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Cited by 59 (10 self)
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new data sets of the global distribution and properties of aerosol are being retrieved, and need to be validated and analyzed. A system has been put in place to generate spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of the MODIS aerosol parameters over more than 100 validation sites spread around the globe. Corresponding statistics are also computed from temporal subsets of AERONETderived aerosol data. The means and standard deviations of identical parameters from MODIS and AERONET are compared. Although, their means compare favorably, their standard deviations reveal some influence of surface effects on the MODIS aerosol retrievals over land, especially at low aerosol loading. The direction and rate of spatial variation from MODIS are used to study the spatial distribution of aerosols at various locations either individually or comparatively. This paper introduces the methodology for generating and analyzing the data sets used by the two MODIS aerosol validation papers in this issue. INDEX
Error limiting reductions between classification tasks
 In Proceedings of the International Conference on Machine Learning (ICML
, 2005
"... We introduce a reductionbased model for analyzing supervised learning tasks. We use this model to devise a new reduction from multiclass costsensitive classification to binary classification with the following guarantee: If the learned binary classifier has error rate at most ɛ then the costsens ..."
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Cited by 44 (7 self)
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We introduce a reductionbased model for analyzing supervised learning tasks. We use this model to devise a new reduction from multiclass costsensitive classification to binary classification with the following guarantee: If the learned binary classifier has error rate at most ɛ then the costsensitive classifier has cost at most 2ɛ times the expected sum of costs of all possible lables. Since costsensitive classification can embed any bounded loss finite choice supervised learning task, this result shows that any such task can be solved using a binary classification oracle. Finally, we present experimental results showing that our new reduction outperforms existing algorithms for multiclass costsensitive learning. 1
Ensembles of nested dichotomies for multiclass problems
 In Proc 21st International Conference on Machine Learning
, 2004
"... Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multiclass classification task into a system of dichotomies and provide a statistically sound wa ..."
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Cited by 31 (5 self)
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Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multiclass classification task into a system of dichotomies and provide a statistically sound way of applying twoclass learning algorithms to multiclass problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multiclass problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to errorcorrecting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good generalpurpose method for applying binary classifiers to multiclass problems.
Centersurround antagonism based on disparity in primate area MT
 J. Neurosci
, 1998
"... Most neurons in primate visual area MT have a large, modulatory region surrounding their classically defined receptive field, or center. The velocity tuning of this “surround ” is generally antagonistic to the center, making it potentially useful for detecting image discontinuities on the basis of d ..."
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Cited by 29 (0 self)
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Most neurons in primate visual area MT have a large, modulatory region surrounding their classically defined receptive field, or center. The velocity tuning of this “surround ” is generally antagonistic to the center, making it potentially useful for detecting image discontinuities on the basis of differential motion. Because classical MT receptive fields are also disparityselective, one might expect to find disparitybased surround antagonism as well; this would provide additional information about image discontinuities. However, the effects of disparity in the MT surround have not been studied previously. We measured singleneuron responses to variabledisparity moving patterns in the MT surround while holding a central moving pattern at a fixed disparity. Of the 130 neurons tested, 84% exhibited a modulatory surround, and in 52 % of these, responses were significantly affected by disparity in the surround.
First words in the second year: Continuity, stability, and models of concurrent and predictive correspondence in vocabulary and verbal responsiveness across age and context. Infant Behavior and Development
, 1999
"... This prospective longitudinal study assessed children's and mothers ' productive vocabulary and mothers' verbal responses to children's exploratory and vocal behavior in spontaneous peech, and evaluated multiple relations in those measures in two contexts (play and mealtimes) at ..."
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Cited by 27 (4 self)
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This prospective longitudinal study assessed children's and mothers ' productive vocabulary and mothers' verbal responses to children's exploratory and vocal behavior in spontaneous peech, and evaluated multiple relations in those measures in two contexts (play and mealtimes) at two child ages (13 and 20 months). Continuity, stability, and several models of concurrent and lamed childmother correspondences were evaluated. Child and mother vocabulary increased across the second year, but did so differently in the two contexts; vocabulary of both showed significant stability of individual variation across context and age. Developmental change in maternal verbal responses predicted child vocabulary (maternal vocabulary did not), and developmental change in child vocabulary predicted maternal responses. The results support a model of specificity in motherchild language xchange and child vocabulary growth.
Error Correcting Tournaments
, 2008
"... Abstract. We present a family of adaptive pairwise tournaments that are provably robust against large error fractions when used to determine the largest element in a set. The tournaments use nk pairwise comparisons but have only O(k + log n) depth, where n is the number of players and k is the robus ..."
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Cited by 26 (4 self)
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Abstract. We present a family of adaptive pairwise tournaments that are provably robust against large error fractions when used to determine the largest element in a set. The tournaments use nk pairwise comparisons but have only O(k + log n) depth, where n is the number of players and k is the robustness parameter (for reasonable values of n and k). These tournaments also give a reduction from multiclass to binary classification in machine learning, yielding the best known analysis for the problem. 1
DAnTE: A statistical tool for quantitative analysis of omics data
 Bioinformatics
"... Summary: DAnTE (Data Analysis Tool Extension) is a statistical tool designed to address challenges associated with quantitative bottomup, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other highthroughput data types. DAnTE features ..."
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Summary: DAnTE (Data Analysis Tool Extension) is a statistical tool designed to address challenges associated with quantitative bottomup, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other highthroughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide to protein rollup methods, an extensive array of plotting functions, and a comprehensive hypothesis testing scheme that can handle unbalanced data and random effects. The Graphical User Interface (GUI) is designed to be very intuitive and user friendly. Availability: DAnTE may be downloaded free of charge at