Improved shrinkage testimators for the simple linear regression model

Al-Hemyari, Zuhair A.; Hussain, Ishfaq

doi:10.1504/ijids.2012.045129

Cited by 6 publications

(6 citation statements)

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“…A ready-made answer is given through the use of the preliminary test shrunken (PTS) procedure, which assures utilizing u o in new estimation problems of u and R(t), getting the estimators easily and quickly and working on improving the quality of the estimators. 15,16 The conventional PTS, the estimator of u, is defined as follows: let T i , i = 1, 2, . .…”

Section: Incorporating a Guess Value And Preliminary Test Shrunkenmentioning

confidence: 99%

“…2. Computeû 1 (by using equation (4)), c = ffiffiffiffiffiffiffiffiffiffiffi e Àq d 1 p , a 1 = ½q=(1 + c), b 2 = ½q=(1 À c) (see equations (9), (15) and (16)) and 1 R 1 = ½a 1 , b 1 . 3.…”

Section: Derivation Of the First Proposed Estimatormentioning

confidence: 99%

“…The regions 1 R 2 and 2 R 2 are also defined by the equations (15) and (18), respectively. Proceeding in the same manner as in the last section, we will get…”

Section: Derivation Of the Second Modified Pts Estimatormentioning

confidence: 99%

“…The optimum value of k that minimizes the risk depends on the unknown parameter u, and therefore, it has to be estimated from the censored data. The expression of k and thenk is too complicated, 15,16 which is one of the limitations of this procedure. Thus, empirical choice of k is recommended.…”

Section: Limitationsmentioning

confidence: 99%

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Two modified estimators of reliability function with exponential failure model based on prior information and time-censored data

Al-Hemyari

Al-Dolami

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this article, we have assumed that the observations are carried out for a period of time T, and when a unit failed, it was replaced with a new identical unit. We have considered the exponential distribution of failure, and we have proposed two estimators of reliability function (R(t)) by using time-censored data when a prior value (uncertain prior information) about the unknown parameter (u) was previously available. The expressions for the bias, risk and relative efficiency were obtained. The investigation concerning the improvement of the proposed estimators of R(t) on the bases of the criterion of bias ratio, risk and relative efficiency under different cases showed that the proposed estimators were better than the classical estimators. The conclusions, concluding remarks and limitations were then reported.

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Section: Incorporating a Guess Value And Preliminary Test Shrunkenmentioning

confidence: 99%

Section: Derivation Of the First Proposed Estimatormentioning

confidence: 99%

“…The regions 1 R 2 and 2 R 2 are also defined by the equations (15) and (18), respectively. Proceeding in the same manner as in the last section, we will get…”

Section: Derivation Of the Second Modified Pts Estimatormentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Two modified estimators of reliability function with exponential failure model based on prior information and time-censored data

Al-Hemyari

Al-Dolami

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Probabilities calculation is carried out in this technique by a logistic model equation. More detail of the algorithms can be found at [15,[18][19][20][21]].…”

Section: Statistical Techniquesmentioning

confidence: 99%

A Benchmark to Select Data Mining Based Classification Algorithms for Business Intelligence and Decision Support Systems

Kumar¹

2012

IJDKP

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In today's business scenario, we percept major changes in how managers use computerized support in making decisions. As more number of decision-makers use computerized support in decision making, decision support systems (DSS) is developing from its starting as a personal support tool and is becoming the common resource in an organization. DSS serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance. Data mining has a vital role to extract important information to help in decision making of a decision support system. It has been the active field of research in the last two-three decades. Integration of data mining and decision support systems (DSS) can lead to the improved performance and can enable the tackling of new types of problems. Artificial Intelligence methods are improving the quality of decision support, and have become embedded in many applications ranges from ant locking automobile brakes to these days interactive search engines. It provides various machine learning techniques to support data mining. The classification is one of the main and valuable tasks of data mining. Several types of classification algorithms have been suggested, tested and compared to determine the future trends based on unseen data. There has been no single algorithm found to be superior over all others for all data sets. Various issues such as predictive accuracy, training time to build the model, robustness and scalability must be considered and can have tradeoffs, further complex the quest for an overall superior method. The objective of this paper is to compare various classification algorithms that have been frequently used in data mining for decision support systems. Three decision trees based algorithms, one artificial neural network, one statistical, one support vector machines with and without adaboost and one clustering algorithm are tested and compared on four datasets from different domains in terms of predictive accuracy, error rate, classification index, comprehensibility and training time. Experimental results demonstrate that Genetic Algorithm (GA) and support vector machines based algorithms are better in terms of predictive accuracy. Former shows highest comprehensibility but is slower than later. From the decision tree based algorithms, QUEST produces trees with lesser breadth and depth showing more comprehensibility. This research work shows that GA based algorithm is more powerful algorithm and shall be the first choice of organizations for their decision support systems. SVM without adaboost shall be the first choice in context of speed and predictive accuracy. Adaboost improves the accuracy of SVM but on the cost of large training time.Pardeep Kumar received his M.Tech (CSE) degree from Guru Jambheshwar

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A class of efficient and modified testimators for the mean of normal distribution using complete data

Al-Hemyari

Hussain

Al-Jobori

2011

IJDATS

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Improved shrinkage testimators for the simple linear regression model

Cited by 6 publications

References 0 publications

Two modified estimators of reliability function with exponential failure model based on prior information and time-censored data

Two modified estimators of reliability function with exponential failure model based on prior information and time-censored data

A Benchmark to Select Data Mining Based Classification Algorithms for Business Intelligence and Decision Support Systems

A class of efficient and modified testimators for the mean of normal distribution using complete data

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