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Maximum score estimation of the stochastic utility model of choice
 Journal of Econometrics
, 1975
"... This paper introduces a class of robust estimators of the parameters of a stochastic utility function. Existing maximum likelihood and regression estimation methods require the assumption of a particular distributional family for the random component of utility. In contrast, estimators of the ‘maxi ..."
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Cited by 204 (2 self)
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of the ‘maximum score ’ class require only weak distributional assumptions for consistency. Following presentation and proof of the basic consistency theorem, additional results are given. An algorithm for achieving maximum score estimates and some small sample Monte Carlo tests are also described. 1.
Maximum Score Methods
"... In a seminal paper, Manski (1975) introduces the Maximum Score Estimator (MSE) of the structural parameters of a multinomial choice model and proves consistency without assuming knowledge of the distribution of the error terms in the model. As such, the MSE is the rst instance of a semiparametric e ..."
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In a seminal paper, Manski (1975) introduces the Maximum Score Estimator (MSE) of the structural parameters of a multinomial choice model and proves consistency without assuming knowledge of the distribution of the error terms in the model. As such, the MSE is the rst instance of a semiparametric
SMOOTHED MAXIMUM SCORE ESTIMATOR
, 1996
"... The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and asymptotically normal under weak distributional assumptions. However, the differences between the true and nominal levels of tests based on smoothed maximum score estimates can be very large i ..."
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The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and asymptotically normal under weak distributional assumptions. However, the differences between the true and nominal levels of tests based on smoothed maximum score estimates can be very large
MaximumScoring Segment Sets
"... Abstract — We examine the problem of finding maximumscoring sets of disjoint segments in a sequence of scores. The problem arises in DNA and protein segmentation, and in postprocessing of sequence alignments. Our key result states a simple recursive relationship between maximumscoring segment sets. ..."
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Abstract — We examine the problem of finding maximumscoring sets of disjoint segments in a sequence of scores. The problem arises in DNA and protein segmentation, and in postprocessing of sequence alignments. Our key result states a simple recursive relationship between maximumscoring segment sets
Semiparametric Analysis of Discrete Response: Asymptotic Properties of Maximum Score
 Estimation,Journal of Econometrics
, 1985
"... This paper is concerned with the estimation of the model MED ( y 1 x) = x/3 from a random sample of observations on (sgn y, x). Manski (1975) introduced the maximum score estimator of the normalized parameter vector /3 * = /3/]]8]]. In the present paper, strong consistency is proved. It is also pr ..."
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Cited by 164 (1 self)
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This paper is concerned with the estimation of the model MED ( y 1 x) = x/3 from a random sample of observations on (sgn y, x). Manski (1975) introduced the maximum score estimator of the normalized parameter vector /3 * = /3/]]8]]. In the present paper, strong consistency is proved. It is also
Computing MaximumScoring Segments Optimally
"... Given a sequence of length n, the problem studied in this paper is to find a set of k disjoint subsequences of consecutive elements such that the total sum of all elements in the set is maximized. This problem arises in the analysis of DNA sequences. The previous best known algorithm requires Θ(nα(n ..."
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Given a sequence of length n, the problem studied in this paper is to find a set of k disjoint subsequences of consecutive elements such that the total sum of all elements in the set is maximized. This problem arises in the analysis of DNA sequences. The previous best known algorithm requires Θ(nα(n, n)) time in the worst case, where α(n, n) is the inverse Ackermann function. We present a lineartime algorithm, which is optimal, for this problem. 1
ASYMPTOTICS FOR MAXIMUM SCORE METHOD UNDER GENERAL CONDITIONS
"... Abstract. Since Manski’s (1975) seminal work, the maximum score method for discrete choice models has been applied to various econometric problems. Kim and Pollard (1990) established the cube root asymptotics for the maximum score estimator. Since then, however, econometricians posed several open qu ..."
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Abstract. Since Manski’s (1975) seminal work, the maximum score method for discrete choice models has been applied to various econometric problems. Kim and Pollard (1990) established the cube root asymptotics for the maximum score estimator. Since then, however, econometricians posed several open
Computing maximumscoring segments in almost linear time
 IN PROCEEDINGS OF THE 12TH ANNUAL INTERNATIONAL COMPUTING AND COMBINATORICS CONFERENCE, VOLUME 4112 OF LNCS
, 2006
"... Given a sequence, the problem studied in this paper is to find a set of k disjoint continuous subsequences such that the total sum of all elements in the set is maximized. This problem arises naturally in the analysis of DNA sequences. The previous best known algorithm requires Θ(n log n) time in th ..."
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Cited by 3 (1 self)
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Given a sequence, the problem studied in this paper is to find a set of k disjoint continuous subsequences such that the total sum of all elements in the set is maximized. This problem arises naturally in the analysis of DNA sequences. The previous best known algorithm requires Θ(n log n) time in the worst case. For a given sequence of length n, we present an almost lineartime algorithm for this problem. Our algorithm uses a disjointset data structure and requires O(nα(n, n)) time in the worst case, where α(n, n) is the inverse Ackermann function.
A Smoothed Maximum Score Estimator for Multinomial Discrete Choice Models
, 2012
"... We propose a semiparametric estimator for multinomial discrete choice models. The term “semiparametric ” refers to the fact that we do not specify a particular functional form for the error term in the random utility function and we allow for heteroskedasticity and serial correlation. Despite being ..."
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semiparametric, the rate of convergence of the smoothed maximum score estimator is not affected by the number of alternative choices and does not suffer from the “curse of dimensionality”. We show the strong consistency and asymptotic normality of the smoothed maximum score estimator for multinomial discrete
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
 ADVANCES IN LARGE MARGIN CLASSIFIERS
, 1999
"... The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
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Cited by 1051 (0 self)
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The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score
Results 1  10
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2,791