Results 11 to 20 of about 3,051,269 (303)
Grid-based simulation and the method of conditional least squares [PDF]
Proceedings of the 28th conference on Winter simulation - WSC '96, 1996 This paper is concerned with the use of simulation to compute the conditional expectations that arise in the method of conditional least squares. Our approach involves performing simulations at each point on a discrete grid imbedded within a statistical parameter space.
Katherine B. Ensor, Peter W. Glynn
openaire +3 more sources
Condition numbers of the mixed least squares-total least squares problem revisited
Linear and Multilinear Algebra, 2022 20 ...
Qiaohua Liu, Qian Zhang, Dongmei Shen
openaire +2 more sources
A Contribution to the Conditioning of the Total Least-Squares Problem [PDF]
SIAM Journal on Matrix Analysis and Applications, 2011 We derive closed formulas for the condition number of a linear function of the total least squares solution. Given an over determined linear system Ax=b, we show that this condition number can be computed using the singular values and the right singular vectors of [A,b] and A.
Baboulin, Marc, Gratton, Serge
openaire +6 more sources
On the condition number of the total least squares problem [PDF]
Numerische Mathematik, 2013 27 pages, 2 figures.
Zhongxiao Jia, Bingyu Li
openaire +3 more sources
Computing the conditioning of the components of a linear least‐squares solution [PDF]
Numerical Linear Algebra with Applications, 2008 AbstractIn this paper, we address the accuracy of the results for the overdetermined full rank linear least‐squares problem. We recall theoretical results obtained in (SIAM J. Matrix Anal. Appl. 2007; 29(2):413–433) on conditioning of the least‐squares solution and the components of the solution when the matrix perturbations are measured in Frobenius ...
Baboulin, Marc +3 more
openaire +5 more sources
The conditioning of least‐squares problems in variational data assimilation [PDF]
Numerical Linear Algebra with Applications, 2018 SummaryIn variational data assimilation a least‐squares objective function is minimised to obtain the most likely state of a dynamical system. This objective function combines observation and prior (or background) data weighted by their respective error statistics.
Jemima M. Tabeart +5 more
openaire +5 more sources
Least-Squares Conditional Density Estimation
IEICE Transactions on Information and Systems, 2010 Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful.
Sugiyama, M. +5 more
openaire +2 more sources
First-Order Integer-Valued Moving Average Process with Power Series Innovations
Journal of Statistical Theory and Applications (JSTA), 2020 In this paper, we introduce a first-order nonnegative integer-valued moving average process with power series innovations based on a Poisson thinning operator (PINMAPS(1)) for modeling overdispersed, equidispersed and underdispersed count time series ...
Eisa Mahmoudi, Ameneh Rostami
doaj +1 more source
Asymptotic behaviour of estimators of the parameters of nearly unstable INAR(1) models [PDF]
, 2003 A sequence of first-order integer-valued autoregressive type (INAR(1)) processes is investigated, where the autoregressive type coefficients converge to 1.
van Zuijlen, Martien C. A. +2 more
core +2 more sources
Maximum-Likelihood Estimation in a Special Integer Autoregressive Model
Econometrics, 2020 The paper is concerned with estimation and application of a special stationary integer autoregressive model where multiple binomial thinnings are not independent of one another.
Robert C. Jung, Andrew R. Tremayne
doaj +1 more source