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Results 1 - 10
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136,919
Finding structure in time
- COGNITIVE SCIENCE
, 1990
"... Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a pro ..."
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Cited by 2071 (23 self)
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Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a
A Model of Investor Sentiment
- Journal of Financial Economics
, 1998
"... Recent empirical research in finance has uncovered two families of pervasive regularities: underreaction of stock prices to news such as earnings announcements, and overreaction of stock prices to a series of good or bad news. In this paper, we present a parsimonious model of investor sentiment, or ..."
Abstract
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Cited by 777 (32 self)
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, or of how investors form beliefs, which is consistent with the empirical findings. The model is based on psychological evidence and produces both underreaction and overreaction for a wide range of parameter values. � 1998 Elsevier Science S.A. All rights reserved. JEL classification: G12; G14
Modeling and Forecasting Realized Volatility
, 2002
"... this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly right-skewed, the distributions of the logarithms of realized volatilities are a ..."
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Cited by 549 (50 self)
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-frequency models, we find that our simple Gaussian VAR forecasts generally produce superior forecasts. Furthermore, we show that, given the theoretically motivated and empirically plausible assumption of normally distributed returns conditional on the realized volatilities, the resulting lognormal-normal mixture
Modeling TCP Throughput: A Simple Model and its Empirical Validation
, 1998
"... In this paper we develop a simple analytic characterization of the steady state throughput, as a function of loss rate and round trip time for a bulk transfer TCP flow, i.e., a flow with an unlimited amount of data to send. Unlike the models in [6, 7, 10], our model captures not only the behavior of ..."
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Cited by 1337 (36 self)
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In this paper we develop a simple analytic characterization of the steady state throughput, as a function of loss rate and round trip time for a bulk transfer TCP flow, i.e., a flow with an unlimited amount of data to send. Unlike the models in [6, 7, 10], our model captures not only the behavior
What is a hidden Markov model?
, 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with ho-mologous resi ..."
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Cited by 1344 (8 self)
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splice site consenses, codon bias, exon/intron length preferences, and open reading frame analysis all in one scoring system. How should all those parameters be set? How should different kinds of information be weighted? A second issue is being able to interpret results probabilistically. Finding a best
Class-Based n-gram Models of Natural Language
- Computational Linguistics
, 1992
"... We address the problem of predicting a word from previous words in a sample of text. In particular we discuss n-gram models based on calsses of words. We also discuss several statistical algoirthms for assigning words to classes based on the frequency of their co-occurrence with other words. We find ..."
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Cited by 986 (5 self)
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We address the problem of predicting a word from previous words in a sample of text. In particular we discuss n-gram models based on calsses of words. We also discuss several statistical algoirthms for assigning words to classes based on the frequency of their co-occurrence with other words. We
Wide-Area Traffic: The Failure of Poisson Modeling
- IEEE/ACM TRANSACTIONS ON NETWORKING
, 1995
"... Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 wide-area traces, investigating a number of wide-area TCP arrival processes (session and con ..."
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Cited by 1775 (24 self)
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and connection arrivals, FTP data connection arrivals within FTP sessions, and TELNET packet arrivals) to determine the error introduced by modeling them using Poisson processes. We find that user-initiated TCP session arrivals, such as remotelogin and file-transfer, are well-modeled as Poisson processes
Empirical exchange rate models of the Seventies: do they fit out of sample?
- JOURNAL OF INTERNATIONAL ECONOMICS
, 1983
"... This study compares the out-of-sample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and trade-weighted dollar exch ..."
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Cited by 854 (12 self)
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This study compares the out-of-sample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and trade-weighted dollar
Analysis, Modeling and Generation of Self-Similar VBR Video Traffic
, 1994
"... We present a detailed statistical analysis of a 2-hour long empirical sample of VBR video. The sample was obtained by applying a simple intraframe video compression code to an action movie. The main findings of our analysis are (1) the tail behavior of the marginal bandwidth distribution can be accu ..."
Abstract
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Cited by 548 (6 self)
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be accurately described using "heavy-tailed" distributions (e.g., Pareto); (2) the autocorrelation of the VBR video sequence decays hyperbolically (equivalent to long-range dependence) and can be modeled using self-similar processes. We combine our findings in a new (non-Markovian) source model
Regularization paths for generalized linear models via coordinate descent
, 2009
"... We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic ..."
Abstract
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Cited by 724 (15 self)
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We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the
Results 1 - 10
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136,919
