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Results 11 - 20
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11,740
Linear Regression Limit Theory for Nonstationary Panel Data
- ECONOMETRICA
, 1999
"... This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section Ž n. and time series Ž T. observations. The limit theory allows for both sequential limits, wherein T� � followed by n��, and joint limits where T, n�� simultaneously; and the relationship ..."
Abstract
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Cited by 312 (22 self)
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between these multidimensional limits is explored. The panel structures considered allow for no time series cointegration, heterogeneous cointegration, homogeneous cointegration, and near-homogeneous cointegration. The paper explores the existence of long-run average relations between integrated panel
Common Randomness in Information Theory and Cryptography Part II: CR capacity
- IEEE Trans. Inform. Theory
, 1993
"... The CR capacity of a two-teminal model is defined as the maximum rate of common randomness that the terminals can generate using resources specified by the given model. We determine CR capacity for several models, including those whose statistics depend on unknown parameters. The CR capacity is show ..."
Abstract
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Cited by 306 (13 self)
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The CR capacity of a two-teminal model is defined as the maximum rate of common randomness that the terminals can generate using resources specified by the given model. We determine CR capacity for several models, including those whose statistics depend on unknown parameters. The CR capacity
Object Recognition using Multidimensional Receptive Field Histograms and its Robustness to View Point Changes
, 1996
"... This chapter presents a technique to determine the identity of objects in a scene using multidimensional histograms of the responses of a vector of local linear neighborhood operators (receptive fields). This technique can be used to determine the most probable objects in a scene, independent of the ..."
Abstract
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Cited by 247 (26 self)
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to evaluate the robustness of multidimensional receptive field histograms to view point changes, using the Columbia image database [2]. In this experiment we examine the performance of different filter combinations, histogram matching functions and design parameter of the multidimensional histograms. 1
Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters
- INTERNATIONAL JOURNAL OF COMPUTER VISION
, 1999
"... In this paper the theoretical and practical feasibility of self-calibration in the presence of varying intrinsic camera parameters is under investigation. The paper’s main contribution is to propose a self-calibration method which efficiently deals with all kinds of constraints on the intrinsic came ..."
Abstract
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Cited by 195 (13 self)
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In this paper the theoretical and practical feasibility of self-calibration in the presence of varying intrinsic camera parameters is under investigation. The paper’s main contribution is to propose a self-calibration method which efficiently deals with all kinds of constraints on the intrinsic
Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation
, 1997
"... Image rendering maps scene parameters to output pixel values; animation maps motion-control parameters to trajectory values. Because these mapping functions are usually multidimensional, nonlinear, and discontinuous, #nding input parameters that yield desirable output values is often a painful pr ..."
Abstract
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Cited by 247 (3 self)
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Image rendering maps scene parameters to output pixel values; animation maps motion-control parameters to trajectory values. Because these mapping functions are usually multidimensional, nonlinear, and discontinuous, #nding input parameters that yield desirable output values is often a painful
Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance
- Psychological Bulletin
, 1989
"... Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses " to establish stable and substantively well-fitting baseline models, (b ..."
Abstract
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Cited by 261 (6 self)
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, (b) determining partially invariant measurement parameters, and (c) testing for the invariance of factor covariance and mean structures, given partial measurement invariance. We also show, explicitly, the transformation of parameters from an all-^fto an all-y model specification, for purposes
Wavelet Thresholding via a Bayesian Approach
- J. R. STATIST. SOC. B
, 1996
"... We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most applications. ..."
Abstract
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Cited by 262 (33 self)
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We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most applications
Prior Probabilities
- IEEE Transactions on Systems Science and Cybernetics
, 1968
"... e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probability of success. In realistic problems, both the transformation group analysis and the principle of maximum entropy are needed to determine the prior. The distributions thus found are uniquely determ ..."
Abstract
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Cited by 260 (4 self)
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e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probability of success. In realistic problems, both the transformation group analysis and the principle of maximum entropy are needed to determine the prior. The distributions thus found are uniquely
The development and comparison of robust methods for estimating the fundamental matrix
- International Journal of Computer Vision
, 1997
"... Abstract. This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, M-est ..."
Abstract
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Cited by 266 (10 self)
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-estimators and random sampling, and the paper develops the theory required to apply them to non-linear orthogonal regression problems. Although a considerable amount of interest has focussed on the application of robust estimation in computer vision, the relative merits of the many individual methods are unknown
A scheme for robust distributed sensor fusion based on average consensus
- PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN
, 2005
"... We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihoo ..."
Abstract
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Cited by 257 (3 self)
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We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum
Results 11 - 20
of
11,740
