scholarly journals Calibration-Based Estimators using Different Distance Measures under Two Auxiliary Variables: A Comparative Study

2021 ◽  
Vol 19 (1) ◽  
pp. 2-20
Author(s):  
Piyush Kant Rai ◽  
Alka Singh ◽  
Muhammad Qasim
Keyword(s):  
Mean Squared Error ◽  
Real Life ◽  
Distance Functions ◽  
Distance Measures ◽  
Auxiliary Variables ◽  
Data Set ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data ◽  
Relative Root

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.

scholarly journals Efficient transformed ratio-type estimator using single auxiliary information

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Sana Amjad ◽  
◽  
Muhammad Ismail ◽  
Keyword(s):  
Mean Squared Error ◽  
Order Approximation ◽  
Real Life ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Variance Estimators ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data ◽  
Better Than

This paper provides an efficient transformed ratio-type estimator to estimate the study variable's population variance by utilizing information of a single auxiliary variable under simple random sampling without replacement. The bias and mean squared error of the proposed estimator are derived up-to 1st order approximation. In addition to this, the efficiency comparison of the proposed estimator has been done with traditional ratio-type variance estimator and some other widely used modified ratio-type variance estimators by taking real-life data. A simulation study has also been carried out to see the performance of the proposed estimator. It is worth noticing that our proposed estimator performs better than the competing estimators in real-life data applications as the mean squared error and root mean squared error of our proposed estimator are smaller than the competing estimators. Hence, our proposed estimator is better than existing variance estimators.

scholarly journals Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior

2020 ◽  
Vol 18 (2) ◽  
pp. 2-13
Author(s):  
Oyebayo Ridwan Olaniran ◽  
Mohd Asrul Affendi Abdullah
Keyword(s):  
Empirical Bayes ◽  
Cox Model ◽  
Real Life ◽  
Estimation Procedure ◽  
Time Varying ◽  
Data Set ◽  
Life Data ◽  
Real Life Data ◽  
Time Varying Covariates ◽  
Extended Cox Model

A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.

scholarly journals Transmuted Alpha Power Inverse Rayleigh Distribution: Properties and Application

2019 ◽  
Vol 11 (2) ◽  
pp. 185-194
Author(s):  
A. S. Malik ◽  
S. P. Ahmad
Keyword(s):  
Parameter Estimation ◽  
Potential Application ◽  
Real Life ◽  
Rayleigh Distribution ◽  
Parameter Distribution ◽  
Alpha Power ◽  
Data Set ◽  
Life Data ◽  
Real Life Data ◽  
Proposed Model

This paper proposes a new three parameter-distribution through the technique known as Transmutation. The proposed distribution is named Transmuted Alpha power inverse Rayleigh Distribution. Several important properties of the distribution are derived. The parameter estimation is also carried out. Two real life data set are used at the end to describe the potential application of proposed model.

scholarly journals Graduation of term assurance data using frailty approach

2021 ◽  
Vol 16 (3) ◽  
pp. 2809-2823
Author(s):  
Walter Omonywa Onchere
Keyword(s):  
Real Life ◽  
Mortality Rates ◽  
Frailty Models ◽  
Data Set ◽  
Life Data ◽  
Real Life Data ◽  
Crude Mortality ◽  
Exponentiated Weibull ◽  
Weibull Models ◽  
Fully Bayesian

Frailty models have been used in literature to account for heterogeneity among insureds in-terms of mortality. In this article, we compare the gamma and the non-central gamma as frailty distributions with the exponentiated exponential and exponentiated Weibull as baseline hazards. We adopt a fully Bayesian approach to calibrate the baselines based on crude mortality rates from a major Kenyan insurer. Comparing the gamma-exponentiated Weibull with the non-central gamma-exponentiated Weibull models shows that the non-central gamma provides a good fit to the real life data-set and is therefore recommended for valuation.

scholarly journals A New Kumaraswamy Generalized Family of Distributions with Properties, Applications and Bivariate Extension

2020 ◽  
Author(s):  
Muhammad H. Tahir ◽  
Muhammad Adnan Hussain ◽  
Gauss Cordeiro ◽  
Mahmoud El-Morshedy ◽  
Mohammed S. Eliwa
Keyword(s):  
Maximum Likelihood ◽  
Real Life ◽  
Unit Interval ◽  
Likelihood Method ◽  
Data Sets ◽  
Data Set ◽  
Life Data ◽  
Real Life Data ◽  
The Family ◽  
Family Of Distributions

For bounded unit interval, we propose a new Kumaraswamy generalized (G) family of distributions from a new generator which could be an alternate to the Kumaraswamy-G family proposed earlier by Cordeiro and de-Castro in 2011. This new generator can also be used to develop alternate G-classes such as beta-G, McDonald-G, Topp-Leone-G, Marshall-Olkin-G and Transmuted-G for bounded unit interval. Some mathematical properties of this new family are obtained and maximum likelihood method is used for estimating the family parameters. We investigate the properties of one special model called a new Kumaraswamy-Weibull (NKwW) distribution. Parameter estimation is dealt and maximum likelihood estimators are assessed through simulation study. Two real life data sets are analyzed to illustrate the importance and flexibility of this distribution. In fact, this model outperforms some generalized Weibull models such as the Kumaraswamy-Weibull, McDonald-Weibull, beta-Weibull, exponentiated-generalized Weibull, gamma-Weibull, odd log-logistic-Weibull, Marshall-Olkin-Weibull, transmuted-Weibull, exponentiated-Weibull and Weibull distributions when applied to these data sets. The bivariate extension of the family is proposed and the estimation of parameters is given. The usefulness of the bivariate NKwW model is illustrated empirically by means of a real-life data set.

scholarly journals A Comparative Study for Weighted Rayleigh Distribution

2021 ◽  
Author(s):  
Sofi Mudasir Ahad ◽  
Sheikh Parvaiz Ahmad ◽  
Sheikh Aasimeh Rehman
Keyword(s):  
Maximum Likelihood Method ◽  
Mean Squared Error ◽  
Real Life ◽  
Rayleigh Distribution ◽  
Likelihood Method ◽  
Data Sets ◽  
Posterior Distributions ◽  
Bayes Estimators ◽  
Squared Error ◽  
Real Life Data

In this paper, Bayesian and non-Bayesian methods are used for parameter estimation of weighted Rayleigh (WR) distribution. Posterior distributions are derived under the assumption of informative and non-informative priors. The Bayes estimators and associated risks are obtained under different symmetric and asymmetric loss functions. Results are compared on the basis of posterior risk and mean square error using simulated and real life data sets. The study depicts that in order to estimate the scale parameter of the weighted Rayleigh distribution use of entropy loss function under Gumbel type II prior can be preferred. Also, Bayesian method of estimation having least values of mean squared error gives better results as compared to maximum likelihood method of estimation.

scholarly journals Comparing Calibration Product Type Estimators of Population Mean In Stratified Sampling under Two Constraints Using Different Distance Measures

2021 ◽  
pp. 1-16
Author(s):  
D. N. Ojua ◽  
J. A. Abuchu ◽  
E. O. Ojua ◽  
E. I. Enang
Keyword(s):  
Sample Size ◽  
Distance Measure ◽  
Real Life ◽  
Product Type ◽  
The Other ◽  
Distance Measures ◽  
Exponential Distributions ◽  
Chi Square ◽  
Life Data ◽  
Real Life Data

Calibration approach adjusts the original design weights by incorporating an auxiliary variable into it, to make the estimator be in the form of a regression estimator. This method was employed to propose calibration product type estimators using three distance measures namely; chi-square distance measure, the minimum entropy distance measure and the modified chi-square distance measure using double constraints. The estimators of variances of the proposed estimators were also obtained. An empirical study to ascertain the performance of these estimators using a secondary data set and simulated data under underlying distributional assumptions of Gamma, Normal and Exponential distributions with varying sample sizes of 10%, 15%, 20% and 25% were carried out. The result with the real life data showed that the calibration product type estimator from chi-square distance measure estimated the population mean with minimum bias than and obtained from the other distance measures. The result from real life data also revealed that the estimator obtained from chi-square distance measure under two constraints was more efficient than the other three estimators. The result from simulation studies showed that the proposed calibration product type estimators outperform the conventional product type estimator in term of efficiency, consistency and reliability under the Gamma and Exponential distributions with the exponential distribution taking the lead. The conventional product type estimator however was found to be better under normal distribution. It was also observed that as sample size increases there was no significant change in the performance of these proposed estimators which justifies the preference with small sample size.

Integrating User Reviews and Ratings for Enhanced Personalized Searching

2017 ◽  
Vol 15 (2) ◽  
pp. 86-101
Author(s):  
Shuyue Hu ◽  
Yi Cai ◽  
Ho-fung Leung ◽  
Dongping Huang ◽  
Yang Yang
Keyword(s):  
Information Needs ◽  
Real Life ◽  
Experimental Results ◽  
User Profiling ◽  
Basic Information ◽  
Data Set ◽  
User Reviews ◽  
Life Data ◽  
Real Life Data ◽  
Ranking Strategy

With the development of e-commerce, websites such as Amazon and eBay have become very popular. Users post reviews of products and rate the helpfulness of reviews on these websites. Reviews written by a user and reviews rated by a user reflect the user's interests and disinterest. Thus, they are very useful for user profiling. In this study, the authors explore users' reviews and ratings of reviews for personalized searching and propose a review-based user profiling method. To satisfy a user's basic information needs, expressed in the form of a query, they also propose a priority-based result ranking strategy. For evaluation, they conduct experiments on a real-life data set. The experimental results show that their method can significantly improve retrieval quality.

scholarly journals Maintainability Analysis of Underground Mining Equipment Using Genetic Algorithms: Case Studies with an LHD Vehicle

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sihong Peng ◽  
Nick Vayenas
Keyword(s):  
Genetic Algorithms ◽  
Case Studies ◽  
Underground Mining ◽  
Real Life ◽  
Mining Equipment ◽  
Data Set ◽  
Life Data ◽  
Time Period ◽  
Real Life Data ◽  
Maintainability Analysis

While increased mine mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require a significant amount of extra capital investment. This paper deals with aspects of maintainability prediction for mining machinery. A PC software called GenRel was developed for this purpose. In GenRel, it is assumed that failures of mining equipment caused by an array of factors follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest using genetic algorithms (GAs) coupled with a number of statistical techniques. A group of case studies focuses on maintainability analysis of a Load Haul Dump (LHD) vehicle with two different time intervals, three months and six months. The data was collected from an underground mine in the Sudbury area in Ontario, Canada. In each prediction case study, a statistical test is carried out to examine the similarity between the predicted data set with the real-life data set in the same time period. The objectives of case studies include an assessment of the applicability of GenRel using real-life data and an investigation of the impacts of data size and chronological sequence on prediction results.

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