Sri Lankan Journal of Applied Statistics Latest Articleshttps://sljastats.sljol.info/articles/Latest articles published by Sri Lankan Journal of Applied Statisticsen-usFri, 07 Aug 2020 21:33:13 -0000On The Bayesian Analysis of Censored Mixture of Two Topp-Leone Distributionhttps://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7993This paper develops a Bayesian analysis in the context of non-informative priors for the shape parameter of the mixture of Topp-Leone using the censored data. A population of certain objects is assumed to be composed of two subgroups mixed together in an unknown proportion. The random observation taken from this population is supposed to be characterized by one of the two distinct unknown members of a Topp-Leone distribution. We model the heterogeneous population using two components mixture of the Topp-Leone distribution. A comprehensive simulation scheme has been carried out to highlight the properties and behavior of the estimates in terms of sample size, corresponding risks and the mixing weights. A censored mixture data is simulated by probabilistic mixing for the computational purpose. The Bayes estimators of the said parameters have been derived under the assumption of non-informative priors using different loss functions. Posterior risks of the Bayes estimators are compared to explore the effect of prior information and loss functions. Bayes estimators assuming the uniform prior have been observed performing better. Published on 2019-12-31 03:37:26https://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7993Joint Multilevel Model for Analyzing Length of Stay through Competing Endpoints in Dengue Epidemiologyhttps://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7997Dengue is a common mosquito-borne tropical disease caused by a virus. It is a life threatening disease since it sometimes leads to death within a short period of time. Multilevel modeling is a form of statistical modeling when data is at different levels. Due to dengue seriousness and risk being more similar for patients within districts than between districts, there is correlation between patients within districts. Thus district has to be taken as a cluster variable. A frequently encountered response in epidemiological studies is the length of Stay (LOS) of a patient, which measures the time until the event of interest occurs. Complexity arises with the different states/destinations of the time event and competing risk modelling is a more appropriate method for handling such states. The association of platelet count and length of stay of a dengue patient leads to the joint modelling approach for analyzing the dengue patients. Formulation criteria for the joint model with clustered data is to link these models through two sub models that is by using the multilevel multinomial logistic model for the LOS of dengue patients with different destinations and multilevel continuous model for the log platelet count. The linkage between two responses was derived by sharing a common random effect. Factors that have an effect on different destinations of LOS are, time indicators, year, age, classification, rainfall, temperature and humidity, while age, sex, classification, year place treated, rainfall, temp and humidity are associated factors for the log platelet count of dengue patients. Moreover, supremacy of joint modelling was proved by the AIC and BIC values over two separate univariate models. Published on 2019-12-31 03:37:16https://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7997On Some Properties and Applications of Intervened Gegenbauer Distributionhttps://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7926In this paper, an intervened version of the Gegenbauer distribution is considered and investigated some of its statistical properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated using real life data sets. The likelihood ratio test procedure is applied for examining the significance of the intervention parameters and a simulation study is carried out for assessing the performances of the maximum likelihood estimators. Published on 2019-12-31 03:37:06https://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7926Recovering Fisher-Information from the MGF Alone without Requiring Explicit PMF or PDF from a One-Parameter Exponential Familyhttps://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7984It is well-known that a finite <em>moment generating function</em> (m.g.f.) corresponds to a unique probability distribution. So, an important question arises: Is it possible to obtain an expression of Fisher-information, <em>I<sub>X</sub>(Ɵ)</em>; using the m.g.f. alone, that is without requiring explicitly a <em>probability mass function</em> (p.m.f.) or <em>probability density function</em> (p.d.f.), given that the p.m.f. or p.d.f came from a one-parameter exponential family? We revisit the core of statistical inference by developing a clear link (Theorem 1.1) between the m.g.f. and <em>I<sub>X</sub>(Ɵ)</em>. Illustrations are included. Published on 2019-12-31 03:36:54https://sljastats.sljol.info/article/10.4038/sljastats.v19i1.7984Application of Quick Switching System-1 with Single sampling Plan as reference plan through Minimum Sum of Risks in Determining Economic Ordering Policies under Permissible Delay in Paymentshttps://sljastats.sljol.info/article/10.4038/sljastats.v19i2.7980Conventionally, all Economic Ordering model tacitly assumes that the immediate payment with the shipment of the products. But, in practice, the vendor may allow permissible delay in payments to the buyer. Quality management with minimized cost is the crucial factor for organization’s growth. Inspecting 100% of the products are time-consuming and costly especially when it involves destructive testing or inspection cost is huge. Acceptance sampling plan by attributes provides an effective solution to minimize the cost and consumes less time. Quick Switching System-1with two intensity of inspection is ease to apply as it enables instantaneous switch between normal and tightened inspection depends on the quality of the product. With more reliable products normal inspection is employed and vice versa.QSS-1 plan with minimum sum of risks carries another advantage of reducing the consumer and producer’s risk. With the application of the QSS-1 through minimum sum of risks on the EOQ model with permissible delay in payments buyer and vendor gets minimized cost, minimized risk and less time consuming process. Published on 2018-12-30 00:00:00https://sljastats.sljol.info/article/10.4038/sljastats.v19i2.7980Joint Modeling of Mixed Responses with Bayesian Modeling and Neural Networks: Performance Comparison with Application to Poultry Datahttps://sljastats.sljol.info/article/10.4038/sljastats.v19i2.8019Joint modeling of mixed responses has become a popular research area due to its applicability in many disciplines. The interest of this study is joint modeling of survival and count data. Survival data is continuous in nature with censoring information combined to it, while count is a discrete variable. Due to this fact, joint modeling of these two variables will be a challenging task, but it will provide interesting and improved results than modeling these two variables separately. In this study, the concept of joint modeling of survival and count data has been carried out using two approaches: Bayesian modeling and Neural Networks, in order to compare their performances. The results of an application to the poultry data revealed that the Neural Network has a better fit in general. Published on 2018-12-30 00:00:00https://sljastats.sljol.info/article/10.4038/sljastats.v19i2.8019Efficiency of Neighbouring Designs for First Order Correlated Modelshttps://sljastats.sljol.info/article/10.4038/sljastats.v19i2.8020The comparison of efficiency of Complete and Incomplete Nearest Neighbour Balanced Block Designs over regular block design using average variance, generalized variance and min-max variance with the error term e given in the NNBD model follows using first order correlated models. It is observed that, R<sub>H</sub> and R<sub>D</sub> show increasing efficiency values for direct and neighbour effects (left and right) for MA(1) models. The R<sub>A</sub> and R<sub>G</sub> show neither increasing nor decreasing efficiency values are observed for direct and neighbouring effects for AR(1) and MA(1) models. In the case of ARMA(1,1) model, neither increasing nor decreasing efficiency values have been observed for average variance and generalized variance. The R<sub>E</sub> shows decreasing efficiency values with p in the interval 0.1 to 0.4 for direct and neighbouring effects for AR(1), MA(1) and ARMA(1,1) models. Published on 2018-12-30 00:00:00https://sljastats.sljol.info/article/10.4038/sljastats.v19i2.8020Airline Seats Allocation Optimization Through Revenue Managementhttps://sljastats.sljol.info/article/10.4038/sljastats.v19i2.8021Revenue Management has recently gained a solid recognition in Airline industry. It acts as a strategic and tactic provider to manage the uncertainty in demand for their perishable products in the most profitable manner as possible. The Airline Revenue Management tries to attain an effective seat inventory control by utilizing the forecasts of future bookings, the revenue values related with each fare class, and the booking requests by the passengers which in turn will maximize the total revenue of a flight. This paper attempts to propose a novel approach in optimizing the seat inventory control by jointly utilizing the statistical forecasting together with revenue management. The revenue value associated with each point of sale (origin) has been considered when locating seats for a future departure instead of concerning the revenue values of each fare class. Further, it describes a method to obtain optimal seat protection levels that should be reserved from a lower fare origin for a higher fare origin and the nested structure of booking limits for each fare origin so as to optimize the seat allocation in a future departure. A novel approach using Functional Principal Component Regression (FPCR) was carried out to model and forecast the future demand and revenue value for each origin, using historical bookings and revenue values. The Expected Marginal Seat Revenue (EMSR) decision model was developed to address the uncertainty associated with this forecasted future demand and to gain the nested structure of booking limits. Finally, the forecasted booking limits were updated with actual booking requests prior to the flight departure. At the point of verification, it showed a remarkably maximized total revenue over the existing method. Thus, it is suggested that the optimal seat allocation for a better seat inventory control in airlines can be achieved by jointly utilizing the proposed FPCR and EMSR methods. Published on 2018-12-30 00:00:00https://sljastats.sljol.info/article/10.4038/sljastats.v19i2.8021SAI method for solving job shop sequencing problem under certain and uncertain environmenthttps://sljastats.sljol.info/article/10.4038/sljastats.v18i3.7911In this investigation, we use SAI method (Gupta et al. 2016), for solving sequencing problem when processing time of the machine is certain or uncertain in nature. The procedure adopted for solving the sequencing problems is easiest and involves the minimum numbers of iterations to obtain the sequence of jobs. The uncertainty in data is represented by triangular or trapezoidal fuzzy numbers. Yager’s ranking function approach is used to convert these fuzzy numbers into a crisp at a prescribed value of α. Stepwise SAI method is then used to obtain optimal job sequence for the problem. Further, the result obtained by SAI method is compared with Johnson’s Method. Numerical examples are given to demonstrate the effectiveness of the proposed approach. Published on 2017-12-31 00:00:00https://sljastats.sljol.info/article/10.4038/sljastats.v18i3.7911Fractional transportation problem with non-linear discount costhttps://sljastats.sljol.info/article/10.4038/sljastats.v18i3.7935The generalization of linear programming is a fractional programming where the objective function is a proportion of two linear functions. Likewise, in fractional transportation problem the aim is to optimize or improve the ratio of two cost functions or damage functions or demand functions. Since the ratio of two functions is considered, the fractional programming models become more appropriate for dealing with real life problems. The fractional transportation problem (FTP) plays a very important role in supply management for reducing cost and amending service. In real life, the parameters in the models are rarely known exactly and have to be evaluated. This paper investigates the fractional transportation problem (FTP) with some discount cost that avails during the shipment time. The transportation problem, which is one of integer programming problems, deals with distributing any commodity from any group of 'sources' to any group of destinations or 'sinks' in the most effective way with a given 'supply' and 'demand' constraints. The volume of goods to be transported from one place to another incurs some discount cost that could effectively reduce the shipment cost which is directly related to the profit associated with the shipment. This paper is aimed at studying the optimal solution for the problem has been achieved by using Karush-Kuhn-Tucker (KKT) optimality algorithm. Finally, a numerical example is illustrated to support the algorithm. Published on 2017-12-31 00:00:00https://sljastats.sljol.info/article/10.4038/sljastats.v18i3.7935