Special Issue "Selected Papers from the 10th Tartu Conference on Multivariate Statistics"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 September 2016)

Special Issue Editor

Guest Editor
Assoc. Prof. Dr. Meelis Käärik

Institute of Mathematics and Statistics, University of Tartu, Estonia
Website | E-Mail
Interests: premium estimation and reserving in non-life insurance; approximation of distributions; skewed distributions

Special Issue Information

Dear Colleagues,

The 10th Tartu Conference on Multivariate Statistics will be held in Tartu, Estonia, from 28 June to 1 July, 2016. The conference series has a long tradition starting from 1977, and the anniversary conference covers a wide range of problems in modern multivariate statistics, including statistical learning, estimation and testing problems, multivariate mixed models, multivariate distributions, survey statistics, stochastic models in finance and insurance, experimental design and applications in different areas.

This Special Issue selects excellent insurance and finance related papers from the conference, including both theoretical development of methods and practical application of statistical tools to solve specific problems. We invite investigators to contribute original research articles, as well as review articles, to this Special Issue.

Assoc. Prof. Dr. Meelis Käärik
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle On Comparison of Stochastic Reserving Methods with Bootstrapping
Risks 2017, 5(1), 2; doi:10.3390/risks5010002
Received: 30 September 2016 / Revised: 19 December 2016 / Accepted: 20 December 2016 / Published: 4 January 2017
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Abstract
We consider the well-known stochastic reserve estimation methods on the basis of generalized linear models, such as the (over-dispersed) Poisson model, the gamma model and the log-normal model. For the likely variability of the claims reserve, bootstrap method is considered. In the bootstrapping
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We consider the well-known stochastic reserve estimation methods on the basis of generalized linear models, such as the (over-dispersed) Poisson model, the gamma model and the log-normal model. For the likely variability of the claims reserve, bootstrap method is considered. In the bootstrapping framework, we discuss the choice of residuals, namely the Pearson residuals, the deviance residuals and the Anscombe residuals. In addition, several possible residual adjustments are discussed and compared in a case study. We carry out a practical implementation and comparison of methods using real-life insurance data to estimate reserves and their prediction errors. We propose to consider proper scoring rules for model validation, and the assessments will be drawn from an extensive case study. Full article
Open AccessArticle Estimation of Star-Shaped Distributions
Risks 2016, 4(4), 44; doi:10.3390/risks4040044
Received: 2 September 2016 / Accepted: 18 November 2016 / Published: 30 November 2016
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Abstract
Scatter plots of multivariate data sets motivate modeling of star-shaped distributions beyond elliptically contoured ones. We study properties of estimators for the density generator function, the star-generalized radius distribution and the density in a star-shaped distribution model. For the generator function and the
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Scatter plots of multivariate data sets motivate modeling of star-shaped distributions beyond elliptically contoured ones. We study properties of estimators for the density generator function, the star-generalized radius distribution and the density in a star-shaped distribution model. For the generator function and the star-generalized radius density, we consider a non-parametric kernel-type estimator. This estimator is combined with a parametric estimator for the contours which are assumed to follow a parametric model. Therefore, the semiparametric procedure features the flexibility of nonparametric estimators and the simple estimation and interpretation of parametric estimators. Alternatively, we consider pure parametric estimators for the density. For the semiparametric density estimator, we prove rates of uniform, almost sure convergence which coincide with the corresponding rates of one-dimensional kernel density estimators when excluding the center of the distribution. We show that the standardized density estimator is asymptotically normally distributed. Moreover, the almost sure convergence rate of the estimated distribution function of the star-generalized radius is derived. A particular new two-dimensional distribution class is adapted here to agricultural and financial data sets. Full article
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Open AccessArticle Parameter Estimation in Stable Law
Risks 2016, 4(4), 43; doi:10.3390/risks4040043
Received: 22 September 2016 / Revised: 7 November 2016 / Accepted: 21 November 2016 / Published: 25 November 2016
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Abstract
For general stable distribution, cumulant function based parameter estimators are proposed. Extensive simulation experiments are carried out to validate the effectiveness of the estimates over the entire parameter space. An application to non-life insurance losses distribution is made. Full article
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Figure 1

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