CUSTOMER SATISFACTION MEASUREMENT MODELS: GENERALISED MAXIMUM ENTROPY APPROACH

This paper presents the methodology of the Generalised Maximum Entropy (GME) approach for estimating linear models that contain latent variables such as customer satisfaction measurement models. The GME approach is a distribution free method and it provides better alternatives to the conventional method; Namely, Partial Least Squares (PLS), which used in the context of costumer satisfaction measurement. A simplified model that is used for the Swedish customer satis faction index (CSI) have been used to generate simulated data in order to study the performance of the GME and PLS. The results showed that the GME outperforms PLS in terms of mean square errors (MSE). A simulated data also used to compute the CSI using the GME approach.Generalised Maximum Entropy, Partial Least Squares, Costumer Satisfaction Models.

Estimation Of Multiple Linear Functional Relationships

This article deals with multiple linear functional relationships models. Two robust estimations procedure are proposed to estimate the model, based on Generalized Maximum Entropy and Partial Least Square. They are distribution free and do not rely (so much) on classical assumptions. The experiments showed that the GME approach outperforms the PLS in terms of mean squares of errors (MSE). Empirical examples are studied

Interval Estimation For The Scale Parameter Of Burr Type X Distribution Based On Grouped Data

The application of some bootstrap type intervals for the scale parameter of the Burr type X distribution with grouped data is proposed. The general asymptotic confidence interval procedure (Chen & Mi, 2001) is studied. The performance of these intervals is investigated and compared. Some of the bootstrap intervals give better performance for situations of small sample size and heavy censoring

Estimating The Slope Of Simple Linear Regression In The Presence Of Outliers

In this article, an estimation procedure to simple linear regression in the presence of outliers is proposed. The performance of the proposed estimator, the AM estimator, is compared with other traditional estimators: least squares, Theil type repeated median, and geometric mean. A numerical example is given to illustrate the proposed estimator. Simulation results indicate that the proposed estimator is accurate and has a high precision in the presence of outliers

Emission Rate of Gases Emitted from Private Gasoline Vehicles in Irbid-Jordan

In this paper, we analyze the emissions caused by private gasoline vehicles based on a random sample of 950 vehicles. Different types of pollutants; carbon monoxide (CO), carbon dioxide (CO2) and hydrocarbon (HC) were analyzed based on the Jordanian emission standards with special reference to three different vehicles' manufacturing countries; namely: Korea, Japan and Germany. The analysis results indicated that Korean made vehicles gave better results than Japanese and German made ones on superiority to pass the tests of vehicles' emission. The results indicated that at a significance value of 0.05 there was a statistical relationship between Japanese vehicles and model year, engine capacity, vehicle fuel supply system and periodic maintenance. Also, for German vehicles there was a statistical relationship between manufacturing year, fuel type and fuel supply system. However, Korean vehicles showed a statistical relationship only with the fuel supply system. Overall, all vehicles should have an injection system in order to reduce exhausts' emissions, and the vehicles should be as new as possible. It should also be recommended not to import vehicles with carburetor fuel supply system

Estimation of Gini-index from continuous distribution based on ranked set sampling

This paper introduces the idea of using the novel ranked set sampling scheme for estimating the Gini index from continuous distributions. A one dimensional integral estimation problem based on ranked samples was discussed. It is demonstrated by a simple Monte Carlo experiment that this approach provides an unbiased and more efficient Gini index estimators than the traditional estimators based on simple random sampling

EXTREME RANKED REPETITIVE SAMPLING CONTROL CHARTS

ABSTRACT In this paper, we proposed a new ranked data control chart using repetitive sampling criterion to increase the performance of detecting any shift in mean process. For the comparisons target, the average run length (ARL) of the proposed control chart based on repetitive extreme ranked set sampling computed using exact and estimated parameters. The results showed that the ARL affected negatively by the parameter estimation. Moreover, the performances of the proposed control chart is evaluated and compared with similar control chart that obtained by using different sampling schemes such as the simple random sampling, ranked set sampling, extreme ranked set sampling and repetitive ranked set sampling.. The results showed that the ranked data based control chart outperform the classical control chart in terms of the ARL

New Iterative AM Estimation Procedure for Fitting the Simple Linear Measurement Error Models

This article proposed a modified AM estimation procedure. The procedure uses the grouping estimators iteratively after dividing the sample into clus- ters. Then, the grouping AM procedure used to fit the structural relationship with measurement error considering there is no equation error model. The performance of the iterative grouping estimator is compared with the tra- ditional two group estimators. Simulation study showed that in terms of mean square error the proposed estimator is robustify the traditional two group estimator. A real data analysis for studying the relationships between happiness rate and the corruption perceptions index in the Arabs states is discussed

The Journey from Entropy to Generalized Maximum Entropy

Currently we are witnessing the revaluation of huge data recourses that should be analyzed carefully to draw the right decisions about the world problems. Such big data are statistically risky since we know that the data are combination of (useful) signals and (useless) noise, which considered as unorganized facts that need to be filtered and processed. Using the signals only and discarding the noise means that the data restructured and reorganized to be useful and it is called information. So for any data set, we need only the information. In context of information theory, the entropy is used as a statistical measure to quantify the maximum amount of information in a random event

Economic Design of Acceptance Sampling Plans for Truncated Life Tests Using Three-Parameter Lindley Distribution

A single acceptance sampling plan for the three-parameter Lindley distribution under a truncated life test is developed. For various consumer’s confidence levels, acceptance numbers, and values of the ratio of the experimental time to the specified average lifetime, the minimum sample size important to assert a certain average lifetime are calculated. The operating characteristic (OC) function values as well as the associated producer’s risks are also provided. A numerical example is presented to illustrate the suggested acceptance sampling plans