A comparative study for estimating the parameters of the second order moving average process

EnMoving Average process is a representation of a time series written as a finite linear combination of uncorrelated random variables. Our main interest is to compare a classical estimation method; namely Exact Maximum Likelihood Estimation (EMLE) with the Generalized Maximum Entropy (GME) approach for estimating the parameters of the second order moving average processes. In this paper, in applying EMLE we have to find the exact likelihood function through deriving the probability density function of the series. Differentiating the function with respect to the parameters, we can obtain the exact maximum likelihood estimates. On the other hand, the idea of GME is to write the unknown parameters and error terms as the expected value of some proper probability distributions defined over some supports. We carry a simulation study to compare between the presented estimation techniques

A comparative study on repeated measurements data in the presence of missing data

The occurrence of missing observations is nearly unavoidable in longitudi- nal studies where repeated measurements are taken over time on the same subject who may miss appointments or drop out during the study period. In this article, we use the Gaussian estimating objective function to esti- mate the regression and correlation parameters and handle missing data using multiple imputation. The estimation of these parameters is carried out simultaneously using the iterative Newton-Raphson algorithm and the expectation-maximization algorithm. These ideas are implemented using two real data sets and both algorithms showed comparable results with respect to the standard errors of the parameters of interest

A comparative study for estimating the parameters of the second order moving average process

EnMoving Average process is a representation of a time series written as a finite linear combination of uncorrelated random variables. Our main interest is to compare a classical estimation method; namely Exact Maximum Likelihood Estimation (EMLE) with the Generalized Maximum Entropy (GME) approach for estimating the parameters of the second order moving average processes. In this paper, in applying EMLE we have to find the exact likelihood function through deriving the probability density function of the series. Differentiating the function with respect to the parameters, we can obtain the exact maximum likelihood estimates. On the other hand, the idea of GME is to write the unknown parameters and error terms as the expected value of some proper probability distributions defined over some supports. We carry a simulation study to compare between the presented estimation techniques

MONITORING THE PROCESS MEAN BASED ON QUALITY CONTROL CHARTS USING ON FOLDED RANKED SET SAMPLING

In this paper, we proposed a new quality control chart for the sample mean based on a cost free and anti wasting sampling unit’s scheme known by the folded ranked set sampling (FRSS). The new control charts were compared with the classical control charts when the data obtained by using simple random sampling (SRS) and ranked set sampling (RSS). A simulation study showed that the FRSS based control charts are a good alternative to the RSS based charts and they have smaller average run length (ARL) compared with their counterpart charts using SRS

A unified weighted family of distributions which contains the skew-elliptical family

In this paper, we introduce a new family of multivariate distributions, called the unified weighted family, as a generalization to the skew-elliptical family. We study some properties of the proposed family and show that it subsumes many important subfamilies such as the families arisen from the selection and hidden truncation ideas. Although the proposed family is very general, we focus on the multivariate weighted normal family which is regarded as a promising candidate in statistical inference.In this paper, we introduce a new family of multivariate distributions, called the unified weighted family, as a generalization to the skew-elliptical family. We study some properties of the proposed family and show that it subsumes many important subfamilies such as the families arisen from the selection and hidden truncation ideas. Although the proposed family is very general, we focus on the multivariate weighted normal family which is regarded as a promising candidate in statistical inference

An approach to setting up a national customer satisfaction index: the Jordan case study

The aim of this paper was to develop a national customer satisfaction index (CSI) in Jordan and to derive its theory using generalized maximum entropy. During the course of this research, we conducted two different surveys to complete the framework of this CSI. The first one is a pilot study conducted based on a CSI basket in order to select the main factors that comprise the Jordanian customer satisfaction index (JCSI). Based on two different analyses, namely nonlinear principal component analysis and factor analysis, the explained variances in the first and second dimensions were 50.32 and 16.99% respectively. Also, Cronbach coefficients α in the first and second dimensions were 0.923 and 0.521, respectively. The results of this survey suggests the inclusion of loyalty, complaint, expectation, image and service quality as the main CS factors of our proposed model. The second study is a practical implementation conducted on the Vocational Training Corporation in order to evaluate the proposed JCSI. The results indicated that the suggested components of the proposed model are significant and form a good fitted model. We used the comparative fit index and the normed fit index as goodness-of-fit measures to evaluate the effectiveness of our proposed model. Both measures indicated that the proposed model is a promising one.

Prediction intervals for characteristics of future normal sample under moving ranked set sampling

In this article, we derive prediction intervals for the characteristics of a future sample from normal population when the sample is selected via moving extreme ranked set sampling. We conduct a simulation study to compare these intervals with their counterparts using simple random sampling. Finally, we apply our findings on grassland biodiversity real data set in central Europe

Prediction intervals for characteristics of future normal sample under moving ranked set sampling

In this article, we derive prediction intervals for the characteristics of a future sample from normal population when the sample is selected via moving extreme ranked set sampling. We conduct a simulation study to compare these intervals with their counterparts using simple random sampling. Finally, we apply our findings on grassland biodiversity real data set in central Europe