Browsing by Author "Lone, Showkat Ahmad"

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Citation - WoS: 11
Citation - Scopus: 11
Additive Trinomial Frechet Distribution With Practical Application
(Elsevier, 2022) Sindhu, Tabassum Naz; Jarad, Fahd; Lone, Showkat Ahmad; 234808; 02.02. Matematik; 02. Fen-Edebiyat Fakültesi; 01. Çankaya Üniversitesi
This article presents an innovative model called Additive Trinomial Fre chet (ATF) distribution using six parameters. The indicated model is worthy of modeling survival data with a non-monotonic hazard rate. The statistical characteristics of ATF model such as probability generating function, Renyi, Shannon, Tsallis and Mathai-Houbold entropy, quantile function, order statistics, maximum likelihood estimation, factorial and characteristic function, moment generating function, Stress-Strength analysis are thoroughly discussed. The effectiveness of suggested model is demonstrated by the use of a data set from real life. The suggested model has demonstrated better performance and fits the data used superior than other significant counterparts.
Citation - WoS: 59
Citation - Scopus: 58
Comparative Study of Artificial Neural Network Versus Parametric Method in Covid-19 Data Analysis
(Elsevier, 2022) Colak, Andac Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Alsubie, Abdelaziz; Jarad, Fahd; Shafiq, Anum; 234808; 02.02. Matematik; 02. Fen-Edebiyat Fakültesi; 01. Çankaya Üniversitesi
Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.
Citation - WoS: 19
Citation - Scopus: 18
A Novel Extended Gumbel Type Ii Model With Statistical Inference and Covid-19 Applications
(Elsevier, 2022) Sindhu, Tabassum Naz; Shafiq, Anum; Jarad, Fahd; Lone, Showkat Ahmad; 234808; 02.02. Matematik; 02. Fen-Edebiyat Fakültesi; 01. Çankaya Üniversitesi
Statistical models play an important role in data analysis, and statisticians are constantly looking for new or relatively new statistical models to fit data sets across a wide range of fields. In this study, we used a new alpha power transformation and the Gumbel Type -II distribution to suggest an unique statistical model. The study contains a simulation analysis to determine the parameters' efficiency. Two real-life data sets were utilized to demonstrate the use of novel alpha power Gumbel Type II (NAPGT-II) distribution. NAPGT-II distribution yields a better fit than Weibull, new alpha power exponential, exponentiated Gumbel Type-II, Gumbel Type-II and exponentiated generalized Gumbel Type-II distribution, as evidenced by the data.
Citation - WoS: 13
Citation - Scopus: 15
Some Estimation Methods for Mixture of Extreme Value Distributions With Simulation and Application in Medicine
(Elsevier, 2022) Anwar, Sadia; Sindhu, Tabassum Naz; Jarad, Fahd; Lone, Showkat Ahmad; 234808; 02.02. Matematik; 02. Fen-Edebiyat Fakültesi; 01. Çankaya Üniversitesi
In recent years, statisticians have grown increasingly engaged in research of mixture models, particularly in the previous decade, without adequate consideration of challenge of estimating the parameters of mixture models from a frequentist perspective. Except for maximum likelihood estimation, this study addresses this vacuum by discussing the two other classical methods of estimation for mixture model. We commence by briefly describing the three frequentist approaches, namely maximum likelihood, ordinary, and weighted least squares, and then comparing them through extensive numerical simulations. The model's applicability is illustrated by its application to simulated and real-world data, which yields promising results in terms of enhanced estimation.