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Search Results: 1 - 10 of 214 matches for " Pushpa Wijekoon "
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The McDonald Generalized Beta-Binomial Distribution: A New Binomial Mixture Distribution and Simulation Based Comparison with Its Nested Distributions in Handling Overdispersion
Chandrabose Manoj,Pushpa Wijekoon,Roshan Darshana Yapa
International Journal of Statistics and Probability , 2013, DOI: 10.5539/ijsp.v2n2p24
Abstract: The binomial outcome data are widely encountered in many real world applications. The Binomial distribution often fails to model the binomial outcomes since the variance of the observed binomial outcome data exceeds the nominal Binomial distribution variance, a phenomenon known as overdispersion. One way of handling overdispersion is modeling the success probability of the Binomial distribution using a continuous distribution defined on the standard unit interval. The resultant general class of univariate discrete distributions is known as the class of Binomial mixture distributions. The Beta-Binomial (BB) distribution is a prominent member of this class of distributions. The Kumaraswamy-Binomial (KB) distribution is another recent member of this class. In this paper we focus the emphasis on the McDonald's Generalized Beta distribution of the first kind as the mixing distribution and introduce a new Binomial mixture distribution called the McDonald Generalized Beta-Binomial distribution(McGBB). Some theoretical properties of McGBB are discussed. The parameters of the McGBB distribution are estimated via maximum likelihood estimation technique. A real world dataset is modeled by using the new McGBB mixture distribution, and it is shown that this model gives better fit than its nested models. Finally, an extended simulation study is presented to compare the McGBB distribution with its nested distributions in handling overdispersed binomial outcome data.
Improvement of the Preliminary Test Estimator When Stochastic Restrictions are Available in Linear Regression Model  [PDF]
Sivarajah Arumairajan, Pushpakanthie Wijekoon
Open Journal of Statistics (OJS) , 2013, DOI: 10.4236/ojs.2013.34033
Abstract: Ridge type estimators are used to estimate regression parameters in a multiple linear regression model when multicolinearity exists among predictor variables. When different estimators are available, preliminary test estimation procedure is adopted to select a suitable estimator. In this paper, two ridge estimators, the Stochastic Restricted Liu Estimator and Liu Estimator are combined to define a new preliminary test estimator, namely the Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE). The stochastic properties of the proposed estimator are derived, and the performance of PTSRLE is compared with SRLE in the sense of mean square error matrix (MSEM) and scalar mean square error (SMSE) for the two cases in which the stochastic restrictions are correct and not correct. Moreover the SMSE of PTSRLE based on Wald (WA), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are derived, and the performance of PTSRLE is compared using WA, LR and LM tests as a function of the shrinkage parameter d with respect to the SMSE. Finally a numerical example is given to illustrate some of the theoretical findings.
More on the Preliminary Test Stochastic Restricted Liu Estimator in Linear Regression Model  [PDF]
Sivarajah Arumairajan, Pushpakanthie Wijekoon
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.54035
Abstract: In this paper we compare recently developed preliminary test estimator called Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE) with Ordinary Least Square Estimator (OLSE) and Mixed Estimator (ME) in the Mean Square Error Matrix (MSEM) sense for the two cases in which the stochastic restrictions are correct and not correct. Finally a numerical example and a Monte Carlo simulation study are done to illustrate the theoretical findings.
Optimal Generalized Biased Estimator in Linear Regression Model  [PDF]
Sivarajah Arumairajan, Pushpakanthie Wijekoon
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.55042
Abstract: The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model  [PDF]
Varathan Nagarajah, Pushpakanthie Wijekoon
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.57082
Abstract:

In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the logistic regression model by using Scalar Mean Squared Error (SMSE).

On the Restricted Almost Unbiased Ridge Estimator in Logistic Regression  [PDF]
Nagarajah Varathan, Pushpakanthie Wijekoon
Open Journal of Statistics (OJS) , 2016, DOI: 10.4236/ojs.2016.66087
Abstract: In this article, the restricted almost unbiased ridge logistic estimator (RAURLE) is proposed to estimate the parameter in a logistic regression model with exact linear re-strictions when there exists multicollinearity among explanatory variables. The performance of the proposed estimator over the maximum likelihood estimator (MLE), ridge logistic estimator (RLE), almost unbiased ridge logistic estimator (AURLE), and restricted maximum likelihood estimator (RMLE) with respect to different ridge parameters is investigated through a simulation study in terms of scalar mean square error.
Performance of Existing Biased Estimators and the Respective Predictors in a Misspecified Linear Regression Model  [PDF]
Manickavasagar Kayanan, Pushpakanthie Wijekoon
Open Journal of Statistics (OJS) , 2017, DOI: 10.4236/ojs.2017.75062
Abstract: In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.
User Informatics Optimized Search and Retrieval-Congestion Avoidance Scheme for 4G Networks  [PDF]
Pushpa Pushpa, Sweta Sneha, Rajeev Agrawal
Communications and Network (CN) , 2012, DOI: 10.4236/cn.2012.43026
Abstract: The objective of 4G network is to provide best services to the users which in turn made the performance of existing network more critical. Further, the large traffic generated in such networks creates congestion resulting in overloading of the system. Frequent delays, loss of packets, and in addition the number of retransmission/paging also increases the computational cost of the system. This paper proposes a novel way to reduce overloading and retrieval mechanism for VLR through optimized search, based on the information of users mobility pattern (User profiles based (UPB)) to track the user. This not only improves the overall performance of the system, especially in the events when the visitor location register (VLR) is overloaded due to heavy traffic and congestion of the network. It was also established through simulation studies that the proposed UPB scheme optimizes the search and reduces the average waiting time in a queue. In addition, the provision of VLRW (waiting visitor location register) avoids the overloading of main VLR and provides a recovery/retrieval mechanism for VLR failure.
ICT Proficiency of Dental Students in Sri Lanka
Jayantha Udaya Weerasinghe,Parakrama Wijekoon
Sri Lanka Journal of Bio-Medical Informatics , 2012, DOI: doi: http://dx.doi.org/10.4038/sljbmi.v2i3.3796
Abstract: The Faculty of Dental Sciences, University of Peradeniya has been conducting formal introductory courses in ICT for undergraduates at their entry level. Although senior students do not get an opportunity for ICT education they will be required to perform tasks such as assignments, reports, referencing using ICT tools.Dental undergraduate students’ proficiency in ICT was tested on randomly selected groups from three senior batches. Total performance of all students tested showed that the MCQ score (53.4, SD 8.0) has exceeded the pass mark and practical test (44.0, SD 7.2) has recorded well below. Individual tests on the Practical components also showed that students were extremely weak in Excel (8.1, SD 2.2) and well below passmark in Word (16.9, SD 2.9) and Powerpoint (19.0, SD 5.3) tasks.This indicates that considering the basic nature of the questions in this ICT proficiency test, students have displayed a low level of skill in the practical component. However the total aggregate for the overall student performance (48.8, SD 5.4) has recorded just below the pass mark of 50%. These results reiterate the need for development of a formal supplementary training course in ICT for students in senior batches in the Faculty of Dental Sciences.
ENERGY CONTROLLED EVENT REPORTING IN EVENT-DRIVEN SENSOR NETWORKS
PUSHPA MAMORIYA
International Journal of Engineering Science and Technology , 2012,
Abstract: Wireless sensor networks (WSN) encounter spatially-correlated traffic due to high density of node deployment that is commonly found in detection and tracking applications. Due to spatial correlation among readings of nodes that are observed by a single event, it is not compulsory for every sensor node to transmit its informations to the distant sink node. This paper aims by exploiting the broadcast nature of wireless channel for conserving the energy in spatially-correlated WSN. The reporting energy of an event depends on following factors asresidual energy of node, number of nodes reporting a detected event, and the frequency of occurrence of an event. The collaborate nature of event reporting using multiple nodes reduces power consumption and hence overall network life time will increase. We show that 30 % to 50 % energy saving is achieved by minimizing the redundant information in spatially-correlated WSN.
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