| 1. |
- Antoch, Jaromír, et al.
(författare)
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Recursive robust regression computational aspects and comparison
- 1995
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Ingår i: Computational Statistics & Data Analysis. - 0167-9473 .- 1872-7352. ; 19:2, s. 115-128
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Tidskriftsartikel (refereegranskat)abstract
- The main objective of this paper is to show how algorithms for classical robust regression can be modified for recursive evaluation. It is shown that such a modification can be utilized to increase the algorithmic efficiency for convex object functions. However, for the non-convex ones it is demonstrated that recursion gives little help to find the optimal solution.
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| 2. |
- Eldén, Lars, 1944-
(författare)
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Partial least-squares vs. Lanczos bidiagonalization—I : analysis of a projection method for multiple regression
- 2004
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier. - 0167-9473 .- 1872-7352. ; 46:1, s. 11-31
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Tidskriftsartikel (refereegranskat)abstract
- Multiple linear regression is considered and the partial least-squares method (PLS) for computing a projection onto a lower-dimensional subspace is analyzed. The equivalence of PLS to Lanczos bidiagonalization is a basic part of the analysis. Singular value analysis, Krylov subspaces, and shrinkage factors are used to explain why, in many cases, PLS gives a faster reduction of the residual than standard principal components regression. It is also shown why in some cases the dimension of the subspace, given by PLS, is not as small as desired.
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| 3. |
- Karlsson, Sune, 1960-, et al.
(författare)
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Computationally efficient double bootstrap variance estimation
- 2000
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier. - 0167-9473 .- 1872-7352. ; 33:3, s. 237-247
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Tidskriftsartikel (refereegranskat)abstract
- The double bootstrap provides a useful tool for bootstrapping approximately pivotal quantities by using an "inner" bootstrap loop to estimate the variance. When the estimators are computationally intensive, the double bootstrap may become infeasible. We propose the use of a new variance estimator for the nonparametric bootstrap which effectively removes the requirement to perform the inner loop of the double bootstrap. Simulation results indicate that the proposed estimator produce bootstrap-t confidence intervals with coverage accuracy which replicates the coverage accuracy for the standard double bootstrap.
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| 4. |
- Oke, Thimothy, et al.
(författare)
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Small-sample properties of some tests for unit root with data-based choice of the degree of augmentation
- 1999
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Ingår i: Computational Statistics & Data Analysis. - 0167-9473 .- 1872-7352. ; 30:4, s. 457-469
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Tidskriftsartikel (refereegranskat)abstract
- In the augmented Dickey-Fuller (ADF) regression one usually decides on the level of the "augmentation" prior to the performing of unit root test. This is a purely data-dependent method that uses either some information criteria or some sequential test of significance on parameter estimates. Contrary to earlier beliefs, our analyses reveal that the presence and/or absence of a drift and a time trend in the data generating process has a remarkable effect on the behaviour of the subsequent tests for unit root.
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| 5. |
- Orre, R., et al.
(författare)
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Bayesian neural networks with confidence estimations applied to data mining
- 2000
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Ingår i: Computational Statistics & Data Analysis. - 0167-9473 .- 1872-7352. ; 34:4, s. 473-493
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Tidskriftsartikel (refereegranskat)abstract
- An international database of case reports, each one describing a possible case of adverse drug reactions (ADRs), is maintained by the Uppsala Monitoring Centre (UMC), for the WHO international program on drug safety monitoring. Each report can be seen as a row in a data matrix and consists of a number of variables, like drugs used, ADRs, and other patient data. The problem is to examine the database and find significant dependencies which might be signals of potentially important ADRs, to be investigated by clinical experts. We propose a method by which estimated frequencies of combinations of variables are compared with the frequencies that would be predicted assuming there were no dependencies. The estimates of significance are obtained with a Bayesian approach via the variance of posterior probability distributions. The posterior is obtained by fusing a prior distribution (Dirichlet of dimension 2(n-1)) with a batch of data, which is also the prior used when the next batch of data arrives. To decide whether the joint probabilities of events are different fi-om what would follow from the independence assumption, the information component log(P-ij/(PiPj)) plays a crucial role, and one main technical contribution reported here is an efficient method to estimate this measure, as well as the variance of its posterior distribution, for large data matrices. The method we present is fundamentally an artificial neural network denoted Bayesian confidence propagation neural network (BCPNN). We also demonstrate an efficient way of finding complex dependencies. The method is now (autumn 1998) being routinely used to produce warning signals on new unexpected ADR associations.
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| 6. |
- Abramowicz, Konrad, 1983-, et al.
(författare)
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Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data
- 2022
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier. - 0167-9473 .- 1872-7352. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- Nonparametric bagging clustering methods are studied and compared to identify latent structures from a sequence of dependent categorical data observed along a one-dimensional (discrete) time domain. The frequency of the observed categories is assumed to be generated by a (slowly varying) latent signal, according to latent state-specific probability distributions. The bagging clustering methods use random tessellations (partitions) of the time domain and clustering of the category frequencies of the observed data in the tessellation cells to recover the latent signal, within a bagging framework. New and existing ways of generating the tessellations and clustering are discussed and combined into different bagging clustering methods. Edge tessellations and adaptive tessellations are the new proposed ways of forming partitions. Composite methods are also introduced, that are using (automated) decision rules based on entropy measures to choose among the proposed bagging clustering methods. The performance of all the methods is compared in a simulation study. From the simulation study it can be concluded that local and global entropy measures are powerful tools in improving the recovery of the latent signal, both via the adaptive tessellation strategies (local entropy) and in designing composite methods (global entropy). The composite methods are robust and overall improve performance, in particular the composite method using adaptive (edge) tessellations.
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| 7. |
- Ahmad, M. Rauf
(författare)
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A significance test of the RV coefficient in high dimensions
- 2019
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Ingår i: Computational Statistics & Data Analysis. - : ELSEVIER SCIENCE BV. - 0167-9473 .- 1872-7352. ; 131, s. 116-130
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Tidskriftsartikel (refereegranskat)abstract
- The RV coefficient is an important measure of linear dependence between two multivariate data vectors. Using unbiased and computationally efficient estimators of its components, a modification to the RV coefficient is proposed, and used to construct a test of significance for the true coefficient. The modified estimator improves the accuracy of the original and, along with the test, can be applied to data with arbitrarily large dimensions, possibly exceeding the sample size, and the underlying distribution need only have finite fourth moment. Exact and asymptotic properties are studied under fairly general conditions. The accuracy of the modified estimator and the test is shown through simulations under a variety of parameter settings. In comparisons against several existing methods, both the proposed estimator and the test exhibit similar performance to the distance correlation. Several real data applications are also provided.
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| 8. |
- Andersson, Björn, et al.
(författare)
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Fast estimation of multiple group generalized linear latent variable models for categorical observed variables
- 2023
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier. - 0167-9473 .- 1872-7352. ; 182
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Tidskriftsartikel (refereegranskat)abstract
- A computationally efficient method for marginal maximum likelihood estimation of multiple group generalized linear latent variable models for categorical data is introduced. The approach utilizes second-order Laplace approximations of the integrals in the likelihood function. It is demonstrated how second-order Laplace approximations can be utilized highly efficiently for generalized linear latent variable models by considering symmetries that exist for many types of model structures. In a simulation with binary observed variables and four correlated latent variables in four groups, the method has similar bias and mean squared error compared to adaptive Gauss-Hermite quadrature with five quadrature points while substantially improving computational efficiency. An empirical example from a large-scale educational assessment illustrates the accuracy and computational efficiency of the method when compared against adaptive Gauss-Hermite quadrature with three, five, and 13 quadrature points.
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| 9. |
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| 10. |
- Broström, Göran, 1942-, et al.
(författare)
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Generalized linear models with clustered data : fixed and random effects models
- 2011
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 55:12, s. 3123-3134
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Tidskriftsartikel (refereegranskat)abstract
- The statistical analysis of mixed effects models for binary and count data is investigated. In the statistical computing environment R, there are a few packages that estimate models of this kind. The packagelme4 is a de facto standard for mixed effects models. The packageglmmML allows non-normal distributions in the specification of random intercepts. It also allows for the estimation of a fixed effects model, assuming that all cluster intercepts are distinct fixed parameters; moreover, a bootstrapping technique is implemented to replace asymptotic analysis. The random intercepts model is fitted using a maximum likelihood estimator with adaptive Gauss–Hermite and Laplace quadrature approximations of the likelihood function. The fixed effects model is fitted through a profiling approach, which is necessary when the number of clusters is large. In a simulation study, the two approaches are compared. The fixed effects model has severe bias when the mixed effects variance is positive and the number of clusters is large.
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