Zobrazeno 1 - 10
of 1 133
pro vyhledávání: '"Non-parametric estimation"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-
Externí odkaz:
https://doaj.org/article/0620ba990d3e4d6eae26f9e0b142e364
Publikováno v:
AIMS Mathematics, Vol 8, Iss 12, Pp 28219-28245 (2023)
This article introduces the concept of residual and past Tsallis extropy as a continuous information measure within the context of continuous distribution. Moreover, the characteristics and their relationships with other models are evaluated. Several
Externí odkaz:
https://doaj.org/article/28919123448f4b6fa39bcdcc10a26a9d
Publikováno v:
AIMS Mathematics, Vol 8, Iss 10, Pp 24176-24195 (2023)
In this paper, the Tsallis and Renyi extropy is presented as a continuous measure of information under the continuous distribution. Furthermore, the features and their connection to other information measures are introduced. Some stochastic compariso
Externí odkaz:
https://doaj.org/article/b990b8f4e40f423fa4f05d655192ace1
Autor:
Twine, Edgar Edwin, Ndindeng, Sali Atanga, Mujawamariya, Gaudiose, Adur-Okello, Stella Everline, Kilongosi, Celestine
Publikováno v:
British Food Journal, 2023, Vol. 125, Issue 13, pp. 316-329.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BFJ-08-2022-0698
Publikováno v:
Entropy, Vol 26, Iss 5, p 387 (2024)
Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfe
Externí odkaz:
https://doaj.org/article/8539a03db53e40ad8bc6e80f1a7795f0
Autor:
Parag C. Pendharkar
Publikováno v:
Algorithms, Vol 17, Iss 3, p 111 (2024)
This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probabil
Externí odkaz:
https://doaj.org/article/37ec4d9a327e4d09a4aeb4cb7d4a82e1
Autor:
Jialin Zhang
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 4, Iss 4, Pp 865-887 (2022)
The demands for machine learning and knowledge extraction methods have been booming due to the unprecedented surge in data volume and data quality. Nevertheless, challenges arise amid the emerging data complexity as significant chunks of information
Externí odkaz:
https://doaj.org/article/80fef4768e824b4ca3ae999493a4981a
Autor:
Mohamed A. Abd Elgawad, Haroon M. Barakat, Metwally A. Alawady, Doaa A. Abd El-Rahman, Islam A. Husseiny, Atef F. Hashem, Naif Alotaibi
Publikováno v:
Mathematics, Vol 11, Iss 24, p 4934 (2023)
This study uses an effective, recently extended Farlie–Gumbel–Morgenstern (EFGM) family to derive the distribution of concomitants of K-record upper values (CKRV). For this CKRV, the negative cumulative residual extropy (NCREX), weighted NCREX (W
Externí odkaz:
https://doaj.org/article/aaa885d2e98d44528eb6a3d144799172
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