| 1. |
- Angelov, Angel G., et al.
(författare)
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Nonparametric estimation for self-selected interval data collected through a two-stage approach
- 2017
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Ingår i: Metrika (Heidelberg). - : Springer. - 0026-1335 .- 1435-926X. ; 80:4, s. 377-399
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Tidskriftsartikel (refereegranskat)abstract
- Self-selected interval data arise in questionnaire surveys when respondents are free to answer with any interval without having pre-specified ranges. This type of data is a special case of interval-censored data in which the assumption of noninformative censoring is violated, and thus the standard methods for interval-censored data (e.g. Turnbull's estimator) are not appropriate because they can produce biased results. Based on a certain sampling scheme, this paper suggests a nonparametric maximum likelihood estimator of the underlying distribution function. The consistency of the estimator is proven under general assumptions, and an iterative procedure for finding the estimate is proposed. The performance of the method is investigated in a simulation study.
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| 2. |
- Arnroth, Lukas
(författare)
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Bayesian composite Lp-quanitle regression
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Ingår i: Metrika (Heidelberg). - 0026-1335 .- 1435-926X.
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Tidskriftsartikel (refereegranskat)abstract
- Lp-quantiles are a class of generalized quantiles defined as minimizers of an asymmetric power function. They include both quantiles, p = 1, and expectiles, p = 2, as special cases. This paper studies composite Lp-quantile regression, simultaneously extending single Lp-quantile regression and composite quantileregression. A Bayesian approach is considered, where a novel parameterization of the skewed exponentialpower distribution is utilized. Through a Monte Carlo study and applications to empirical data, theproposed method is shown to outperform Bayesian composite quantile regression in most aspects.
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| 3. |
- Ghorbani, Mohammad
(författare)
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Cauchy cluster process
- 2013
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Ingår i: Metrika (Heidelberg). - : Springer. - 0026-1335 .- 1435-926X. ; 76, s. 697-706
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we introduce an instance of the well-know Neyman–Scott cluster process model with clusters having a long tail behaviour. In our model the offspring points are distributed around the parent points according to a circular Cauchy distribution. Using a modified Cramér-von Misses test statistic and the simulated pointwise envelopes it is shown that this model fits better than the Thomas process to the frequently analyzed long-leaf pine data-set.
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| 4. |
- Karlsson, Maria, 1975-
(författare)
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Estimators of regression parameters for truncated and censored data
- 2006
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Ingår i: Metrika. - : SpringerLink. - 1435-926X .- 0026-1335. ; 63:3, s. 329-341
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Tidskriftsartikel (refereegranskat)abstract
- Estimators of parameters in semi-parametric left truncated and right censored regression models are proposed. In contrast to the majority of existing estimators, the proposed estimators do not require the error term of the regression model to have a symmetric distribution. In addition the estimators use asymmetric “trimming” of observations. Consistency and asymptotic normality of the estimators are shown. Finite sample properties are considered in a small simulation study. For the left truncated case, an empirical application illustrates the usefulness of the estimator.
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| 5. |
- Lee, Myoung-jae, et al.
(författare)
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Trimmed and winsorized semiparametric estimator for left-truncated and right-censored regression models
- 2015
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Ingår i: Metrika (Heidelberg). - : Springer. - 0026-1335 .- 1435-926X. ; 78:4, s. 485-495
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Tidskriftsartikel (refereegranskat)abstract
- For a linear regression model subject to left-truncation and right-censoring where the truncation and censoring points are known constants (or always observed if random), Karlsson and Laitila (Stat Probab Lett 78:2567–2571,2008) proposed a semiparametric estimator which deals with left-truncation by trimming and right-censoring by ‘winsorizing’. The estimator was motivated by a zero moment condition where a transformed error term appears with trimmed and winsorized tails. This paper takes the semiparametric estimator further by deriving the asymptotic distribution that was not shown in Karlsson and Laitila (Stat Probab Lett 78:2567–2571,2008) and discusses its implementation aspects in practice, albeit brief.
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| 6. |
- Tsirpitzi, Renata Eirini, 1991-, et al.
(författare)
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Robust optimal designs using a model misspecification term
- 2023
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Ingår i: Metrika (Heidelberg). - : Springer Science and Business Media LLC. - 0026-1335 .- 1435-926X. ; :86, s. 781-804
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Tidskriftsartikel (refereegranskat)abstract
- Much of classical optimal design theory relies on specifying a model with only a small number of parameters. In many applications, such models will give reasonable approximations. However, they will often be found not to be entirely correct when enough data are at hand. A property of classical optimal design methodology is that the amount of data does not influence the design when a fixed model is used. However, it is reasonable that a low dimensional model is satisfactory only if limited data is available. With more data available, more aspects of the underlying relationship can be assessed. We consider a simple model that is not thought to be fully correct. The model misspecification, that is, the difference between the true mean and the simple model, is explicitly modeled with a stochastic process. This gives a unified approach to handle situations with both limited and rich data. Our objective is to estimate the combined model, which is the sum of the simple model and the assumed misspecification process. In our situation, the low-dimensional model can be viewed as a fixed effect and the misspecification term as a random effect in a mixed-effects model. Our aim is to predict within this model. We describe how we minimize the prediction error using an optimal design. We compute optimal designs for the full model in different cases. The results confirm that the optimal design depends strongly on the sample size. In low-information situations, traditional optimal designs for models with a small number of parameters are sufficient, while the inclusion of the misspecification term lead to very different designs in data-rich cases.
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| 7. |
- Von Rosen, Dietrich
(författare)
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On MLEs in an extended multivariate linear growth curve model
- 2012
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Ingår i: Metrika. - : Springer Science and Business Media LLC. - 0026-1335 .- 1435-926X. ; 75, s. 1069-1092
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Tidskriftsartikel (refereegranskat)abstract
- In this paper the extended growth curve model is considered. The literature comprises two versions of the model. These models can be connected by one-to-one reparameterizations but since estimators are non-linear it is not obvious how to transmit properties of estimators from one model to another. Since it is only for one of the models where detailed knowledge concerning estimators is available (Kollo and von Rosen, Advanced multivariate statistics with matrices. Springer, Dordrecht, 2005) the object in this paper is therefore to present uniqueness properties and moment relations for the estimators of the second model. One aim of the paper is also to complete the results for the model presented in Kollo and von Rosen (Advanced multivariate statistics with matrices. Springer, Dordrecht, 2005). The presented proofs of uniqueness for linear combinations of estimators are valid for both models and are simplifications of proofs given in Kollo and von Rosen (Advanced multivariate statistics with matrices. Springer, Dordrecht, 2005).
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