Results 1 - 10 of 12,670

Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts

by Torben G. Andersen, Tim Bollerslev
"... Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset and derivative pricing theories as well as trading and hedging strategies. In response to this, ..."
Abstract - Cited by 561 (45 self) - Add to MetaCart
volatility persistence. Meanwhile, when judged by standard forecast evaluation criteria, based on the squared or absolute returns over daily or longer forecast horizons, standard volatility models provide seemingly poor forecasts. The present paper demonstrates that, contrary to this contention

Analysis of relative gene expression data using real-time quantitative

by Kenneth J. Livak, Thomas D. Schmittgen - PCR and 2 ���CT method. Methods 25 , 2001
"... of the target gene relative to some reference group The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantifica-such as an untreated control or a sample at time zero tion and relative quantification. Absolute quantification deter- in a time ..."
Abstract - Cited by 2666 (6 self) - Add to MetaCart
time-course study. mines the input copy number, usually by relating the PCR signal Absolute quantification should be performed in situ-to a standard curve. Relative quantification relates the PCR signal ations where it is necessary to determine the absolute of the target transcript in a treatment group

Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems

by Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright - IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING , 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract - Cited by 539 (17 self) - Add to MetaCart
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a

Fibonacci Heaps and Their Uses in Improved Network optimization algorithms

by Michael L. Fredman, Robert Endre Tarjan , 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated F-heaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. F-heaps support arbitrary deletion from an n-item heap in qlogn) amortized tim ..."
Abstract - Cited by 739 (18 self) - Add to MetaCart
time and all other standard heap operations in o ( 1) amortized time. Using F-heaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worst-case bounds, where n is the number of vertices and m the number of edges

Partial Constraint Satisfaction

by Eugene C. Freuder, Richard J. Wallace , 1992
"... . A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying ..."
Abstract - Cited by 471 (21 self) - Add to MetaCart
a maximal number of constraints. Standard backtracking and local consistency techniques for solving constraint satisfaction problems can be adapted to cope with, and take advantage of, the differences between partial and complete constraint satisfaction. Extensive experimentation on maximal

SUS: A quick and dirty usability scale

by John Brooke , 1996
"... Usability does not exist in any absolute sense; it can only be defined with reference to particular contexts. This, in turn, means that there are no absolute measures of usability, since, if the usability of an artefact is defined by the context in which that artefact is used, measures of usability ..."
Abstract - Cited by 397 (1 self) - Add to MetaCart
Usability does not exist in any absolute sense; it can only be defined with reference to particular contexts. This, in turn, means that there are no absolute measures of usability, since, if the usability of an artefact is defined by the context in which that artefact is used, measures of usability

A column approximate minimum degree ordering algorithm

by Timothy A. Davis, et al. , 2000
"... Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero patt ..."
Abstract - Cited by 318 (52 self) - Add to MetaCart
Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero

Eigentaste: A Constant Time Collaborative Filtering Algorithm

by Ken Goldberg, Theresa Roeder, Dhruv Gupta, Chris Perkins , 2000
"... Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clusterin ..."
Abstract - Cited by 378 (6 self) - Add to MetaCart
clustering of users and rapid computation of recommendations. For a database of n users, standard nearest-neighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste requires O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from Jester

Sparse Reconstruction by Separable Approximation

by Stephen J. Wright , Robert D. Nowak , Mário A. T. Figueiredo , 2007
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing ..."
Abstract - Cited by 373 (38 self) - Add to MetaCart
Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing

Least absolute deviations estimation for the censored regression model

by James L. Powell - Journal of Econometrics , 1984
"... This paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression (or censored ‘Tobit’) model. The proposed estimator is a generalization of least absolute deviations estimation for the standard linear model, and, unlike estimation methods based on the ..."
Abstract - Cited by 285 (6 self) - Add to MetaCart
This paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression (or censored ‘Tobit’) model. The proposed estimator is a generalization of least absolute deviations estimation for the standard linear model, and, unlike estimation methods based
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