| 1. |
- Aires, Nibia
(författare)
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A guide to the Fortran programs to calculate inclusion probabilities for conditional Poisson sampling and Pareto pi ps sampling designs
- 2004
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Ingår i: Computational statistics (Zeitschrift). - Heidelberg : Physica Verlag. - 0943-4062 .- 1613-9658. ; 19:3, s. 337-345
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Tidskriftsartikel (refereegranskat)abstract
- Conditional Poisson Sampling and Pareto pips Sampling designs are sampling methods with fixed sample size and with inclusion probabilities proportional to given size measures.. Algorithms were introduced to calculate first and second exact inclusion probabilities for both schemes. Methods were also provided to adjust the parameters to get predetermined inclusion probabilities. In this paper, the Fortran procedures are introduced and documented. Moreover, guidelines are provided for their use as well as examples and the programs codes commented.
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| 2. |
- Allerbo, Oskar, 1985, et al.
(författare)
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Flexible, non-parametric modeling using regularized neural networks
- 2022
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Ingår i: Computational Statistics. - : Springer Science and Business Media LLC. - 0943-4062 .- 1613-9658. ; 37:4, s. 2029-2047
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Tidskriftsartikel (refereegranskat)abstract
- Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation. © 2021, The Author(s).
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| 3. |
- Amiri, Saeid, et al.
(författare)
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Tests of perfect judgment ranking using pseudo-samples
- 2017
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Ingår i: Computational statistics (Zeitschrift). - : Springer Science and Business Media LLC. - 0943-4062 .- 1613-9658. ; 32:4, s. 1309-1322
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Tidskriftsartikel (refereegranskat)abstract
- Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some inferential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set.
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| 4. |
- Andersson, Michael K., et al.
(författare)
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Bootstrapping Error Component Models
- 2001
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Ingår i: Computational statistics (Zeitschrift). - : Physica Verlag. - 0943-4062 .- 1613-9658. ; 16:2, s. 221-231
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap with error component models.
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| 5. |
- Angelov, Angel G., et al.
(författare)
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Quantile regression with interval-censored data in questionnaire-based studies
- 2024
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Ingår i: Computational statistics (Zeitschrift). - : Springer Berlin/Heidelberg. - 0943-4062 .- 1613-9658. ; 39:2, s. 583-603
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Tidskriftsartikel (refereegranskat)abstract
- Interval-censored data can arise in questionnaire-based studies when the respondent gives an answer in the form of an interval without having pre-specified ranges. Such data are called self-selected interval data. In this case, the assumption of independent censoring is not fulfilled, and therefore the ordinary methods for interval-censored data are not suitable. This paper explores a quantile regression model for self-selected interval data and suggests an estimator based on estimating equations. The consistency of the estimator is shown. Bootstrap procedures for constructing confidence intervals are considered. A simulation study indicates satisfactory performance of the proposed methods. An application to data concerning price estimates is presented.
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| 6. |
- Brännäs, Kurt, et al.
(författare)
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Generalized method of moment and indirect estimation of the ARasMA model
- 1998
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Ingår i: Computational statistics (Zeitschrift). - 0943-4062 .- 1613-9658. ; 13:4, s. 485-494
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Tidskriftsartikel (refereegranskat)abstract
- Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both approaches are easy to implement and perform well numerically. In a Monte Carlo study it is found that the MSE properties of GMM are close to those of ML. The indirect estimator performs poorly in this respect. On the other hand, the three estimation techniques lead to fairly similar power functions for a linearity test.
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| 7. |
- Cao, Xiaofeng, et al.
(författare)
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Modelling spatio-temporal variability of temperature
- 2015
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Ingår i: Computational statistics (Zeitschrift). - : Springer. - 0943-4062 .- 1613-9658. ; 30:3, s. 745-766
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Tidskriftsartikel (refereegranskat)abstract
- Forecasting temperature in time and space is an important precondition for both, the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insurance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.
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| 8. |
- Edlund, Ove, et al.
(författare)
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Algorithms for non-linear M-estimation
- 1997
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Ingår i: Computational statistics (Zeitschrift). - 0943-4062 .- 1613-9658. ; 12:3, s. 373-383
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Tidskriftsartikel (refereegranskat)
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| 9. |
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| 10. |
- Gredenhoff, Mikael, et al.
(författare)
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Lag-length selection in VAR-models using equal and unequal lag-length procedures
- 1999
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Ingår i: Computational statistics (Zeitschrift). - : Physica Verlag. - 0943-4062 .- 1613-9658. ; 14:2, s. 171-187
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Tidskriftsartikel (refereegranskat)abstract
- It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIG. In the Monte Carlo study we generate data from a variety of VAR models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure tend to do better than equal lag-length procedures in models with a more complicated lag structure.
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