This introductory text on the design and analysis of sample surveys emphasizes the practical aspects of survey problems. It begins with brief chapters on the role of sample surveys in the modern world. Thereafter, each chapter introduces a sample survey design or estimation procedure by describing the pertinent practical problem. The authors describe the methodology proposed for solving the problem and provide the details of the estimation procedure, including a compact presentation of the formulas needed to complete the analysis. Then, a practical example is worked out in complete detail. At the end of each chapter, a wealth of exercises gives students ample opportunity to practice the techniques and stretch their grasp of ideas.

Elementary Survey Sampling

List of Tables. List of Figures. Introduction. Basic Theory. Moments and Other Expected Values. Linear Estimation Based on Order Statistics. Maximum Likelihood Estimation. Approximate Maximum Likelihood Estimation. Optimal Linear Estimation Based on Selected Order Statistics. Cohen -Whitten Estimators: Using Order Statistics.Estimation in Regression Models. A Sample Completion Technique for Censored Samples. Bibliography. Index.

Order statistics and inference : estimation methods

Ranked set sampling (RSS) is an approach to data collection and analysis that continues to stimulate substantial methodological research. It has spawned a number of related methodologies that are active research arenas as well, and it is finally beginning to find its way into significant applications beyond its initial agricultural-based birth in the seminal paper by McIntyre (1952). In this paper, we provide an introduction to the basic concepts underlying ranked set sampling, in general, with specific illustrations from the one- and two-sample settings. Emphasis is on the breadth of the ranked set sampling approach, with targeted discussion of the many options available to the researcher within the RSS paradigm. The paper also provides a thorough bibliography of the current state of the field and introduces the reader to some of the most promising new methodological extensions of the RSS approach to statistical data analysis.

https://downloads.hindawi.com/archive/2012/568385.pdf

Ranked Set Sampling: Its Relevance and Impact on Statistical Inference

(2005). Ranked Set Sampling: Theory and Applications. Technometrics: Vol. 47, No. 1, pp. 100-101.

Ranked Set Sampling: Theory and Applications

The o ptimal times of changing s tress level for s imple stress plans under a cumulative exp osure model usi ng the Lomax distribution are determined for a wide ra nge o f values o f the model p arameters. The scale parameter of the Lomax failure time distribution at constant levels is assumed to be a log linear function of the st ress level. The o ptimum test p lan i s obtained by minimizing wi th res pect to the change time, the asymptotic variance (AV) of the maximum likelihood estimator (MLE) of a given 50th percentile of the distribution at the design stress. Interval estimations that generate n arrow i ntervals to t he unknown parameters of the distribution wi th h igh probability are obt ained. Also, tests of hypotheses about model parameters using t he l ikelihood ratio method are examined. Numerical study is d iscussed to illustrate t he optimal time p rocedure using M athcad (2001).

Optimum Step Stress Accelerated Life Testing for Lomax Distribution

Optimum simple step-stress plan for log-logistic cumulative exposure model

Non-parametric analysis of a proportional wearout model for accelerated degradation data

Recently, many authors introduced ratio-type estimators for estimating the mean, or the ratio, for a finite populations. Most of the articles are discussing this problem under simple random sampling design, with more assumptions on the auxiliary variable such as the coefficient of variation, and kurtosis are assumed to be known. Gupta and Shabbir (2008) have suggested an alternative form of ratio-type estimators and they assumed the coefficient of variation of the auxiliary variable must be known; this assumption is crucialfor this estimator.An estimator of the population ratio, under general sampling design, is proposed.Further, exact and an unbiased variance estimator of this estimator are obtained, and the Godambe-Joshi lower bound is asymptotically attainable for this estimator. The assumption on the coefficient of variation of the auxiliary variable is not needed for the proposed estimator. Simulation results from real data set and simulations from artificial population, show that the performance of the proposed estimator is better than Gupta and Shabbir (2008) and Hartley and Ross (1954) estimators.

http://www.stat.tugraz.at/AJS/ausg122/122Al-Jararha.pdf

A Ratio Estimator Under General Sampling Design

In this article, we propose a nonparametric estimator for percentiles of the time-to-failure distribution obtained from a linear degradation model using the kernel density method. The properties of the proposed kernel estimator are investigated and compared with well-known maximum likelihood and ordinary least squares estimators via a simulation technique. The mean squared error and the length of the bootstrap confidence interval are used as the basis criteria of the comparisons. The simulation study shows that the performance of the kernel estimator is acceptable as a general estimator. When the distribution of the data is assumed to be known, the maximum likelihood and ordinary least squares estimators perform better than the kernel estimator, while the kernel estimator is superior when the assumption of our knowledge of the data distribution is violated. A comparison among different estimators is achieved using a real data set.

Estimating Percentiles of Time-to-Failure Distribution Obtained from a Linear Degradation Model Using Kernel Density Method

Abstract This paper deals with the optimal design of a degradation experiment, where the degradation rate follows a Weibull distribution. By minimizing the mean squared error of the estimated 100pth percentile of the life time distribution such that the total experimental cost does not exceed a certain budget, the optimal combinations of the sample size, the inspection frequency and the termination time are obtained. The proposed method was applied to a real drug potency data.

Mohammed Al-Haj Ebrahem

Papers

Optimum simple step-stress plan for log-logistic cumulative exposure model

Non-parametric analysis of a proportional wearout model for accelerated degradation data

A Ratio Estimator Under General Sampling Design

Estimating Percentiles of Time-to-Failure Distribution Obtained from a Linear Degradation Model Using Kernel Density Method

Designing degradation experiments using a Weibull distribution