On an analog of empirical distribution for multivariate censored data

Baskakov, V. N.

doi:10.1007/bf02362479

Cited by 1 publication

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the absence of truncation, the estimate generalizes qED, i.e. non-parametric estimate of the distribution function previously proposed in [4] for multivariate censored data. The simple and efficient iterative algorithm for constructing a qED on truncated-censored data has been developed and implemented in the SAS/IML environment for different kinds of univariate and some bivariate sample schemes with truncated and censored observations.…”

Section: Discussionmentioning

confidence: 96%

“…The maximum value of E[ρ] = 0.086 and the same-time maximum median bias equal to (1 − E[δ]) · 100 = −7% is observed in right-censored samples. This fact seems counterintuitive, since in this case the qED is a Kaplan-Meier estimator (see Baskakov [4] for a proof), that is, a widely used estimator with proven good asymptotic properties.…”

Section: Double-truncatedmentioning

confidence: 99%

“…The remaining schemes (3 schemes are censored and one is complete) are included in the simulation program to ensure that the study is complete. The algorithm proposed in the Baskakov's paper [4], is actually used to construct qED based on censored data, and the usual empirical distribution is used for complete data. Therefore, estimates based on complete and censored data can serve as a benchmark when comparing the accuracy of truncated and truncated-censored data.…”

Section: Right-truncatedmentioning

confidence: 99%

“…In this regard, the work of Baskakov [4] shall be considered, which deals with the non-parametric evaluation of the multivariate distribution function based on censored data of almost any data type and proposes a simple iterative algorithm for its construction. However, this estimate fails to extrapolate any truncated observations, which somewhat limits its application in actuarial practice.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we employ Baskakov's idea [4] and generalize his estimate to the case of multivariate truncated-censored data. The work is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Nonparametric estimation of multivariate distribution function for truncated and censored lifetime data

Baskakov

Bartunova²

2019

Eur. Actuar. J.

View full text Add to dashboard Cite

1 A number of models for generating statistical data in various fields of insurance, including life insurance, pensions, and general insurance have been considered. It is shown that the insurance statistics data, as a rule, are truncated and censored, and often multivariate. We propose a non-parametric estimation of the distribution function for multivariate truncated-censored data in the form of a quasi-empirical distribution and a simple iterative algorithm for its construction. To check the accuracy of the proposed evaluation of the distribution function for truncated-censored data, simulation studies have been conducted, which showed its high efficiency. The proposed estimates have been tested for many years by the IAAC Group of Companies in the actuarial valuation of corporate social liabilities according to IAS 19 Employee Benefits. Apart from insurance, some results of the work can be used, for example in medicine, biology, demography, mathematical theory of reliability, etc.

show abstract

Section: Discussionmentioning

confidence: 96%

Section: Double-truncatedmentioning

confidence: 99%

Section: Right-truncatedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this paper, we employ Baskakov's idea [4] and generalize his estimate to the case of multivariate truncated-censored data. The work is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations