Variable Selection for Partially Linear Additive Model Based on Modal Regression Under High Dimensional Data
Issue:
Volume 6, Issue 1, March 2020
Pages:
1-9
Received:
19 December 2019
Accepted:
9 January 2020
Published:
17 April 2020
Abstract: In this article, we focus on the variable selection for partially linear additive model under high dimensional data. Variable selection is proposed based on modal regression estimation with Adoptive Bridge Method. Using the B-spline basic function to approximate the additive function, a penalty estimation objective equation is constructed. It establishes and proves that the variable selection methods have oracle property. Numerical simulations tested the performance of the proposed methods in a finite sample and verified the significance of the proposed estimation and the variable selection methods. At the end of the article, we attach the detailed derivation of the theoretical results. Therefore, the correctness of the method used is verified theoretically and practically.
Abstract: In this article, we focus on the variable selection for partially linear additive model under high dimensional data. Variable selection is proposed based on modal regression estimation with Adoptive Bridge Method. Using the B-spline basic function to approximate the additive function, a penalty estimation objective equation is constructed. It estab...
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Inferences on the Weibull Exponentiated Exponential Distribution and Applications
Umar Usman,
Suleiman Shamsuddeen,
Bello Magaji Arkilla,
Yakubu Aliyu
Issue:
Volume 6, Issue 1, March 2020
Pages:
10-22
Received:
6 November 2019
Accepted:
20 December 2019
Published:
15 July 2020
Abstract: In this article, an alternative method of defining the probability density function of GeneralizedWeibull-exponential distributions is proposed. Based on the method, the distribution can also be calledWeibull exponentiated exponential distribution. This distribution includes the exponential, Weibull and exponentiated exponential distributions as special cases. Comprehensive mathematical treatment of the distribution is provided. The quantile function, mode, characteristic function, moment generating function among other mathematical properties of the distribution were derived. The parameters of the distribution were estimated by applying the Maximum Likelihood Procedure.The elements of the Fisher Information Matrix is also provided. Finally, a data set is fitted to the model and its sub-models. It is observed that the new distribution is more flexible and can be used quiet effectively in analysing real life data in place of exponential, Weibull and exponentiated exponential distributions.
Abstract: In this article, an alternative method of defining the probability density function of GeneralizedWeibull-exponential distributions is proposed. Based on the method, the distribution can also be calledWeibull exponentiated exponential distribution. This distribution includes the exponential, Weibull and exponentiated exponential distributions as sp...
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