An improved cluster two-stage randomized response model

In this paper, Abdelfatah and Mazloum's (2015) two-stage randomized response model is extended to unequal probability sampling and stratified unequal probability sampling, both with and without replacement. The extended models result in more efficient estimators than Lee et al.'s (2014) estimators of the proportion of the population having a sensitive attribute.     

Cytometry inference through adaptive atomic deconvolution

In this paper, we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological data produced by flow cytometers. From a statistical point of view, we aim at inferring the percentage of cells expressing the selected molecule and the probability distribution function associated with its fluorescence emission. We propose here an adaptive estimation procedure based on a previous deconvolution procedure introduced by Es, Gugushvili, and Spreij [(2008), ‘Deconvolution for an atomic distribution’, Electronic Journal of Statistics, 2, 265–297] and Gugushvili, Es, and Spreij [(2011), ‘Deconvolution for an atomic distribution: rates of convergence’, Journal of Nonparametric Statistics, 23, 1003–1029]. For both estimating the mixing parameter and the mixing density automatically, we use the Lepskii method based on the optimal choice of a bandwidth using a bias-variance decomposition. We then derive some convergence rates that are shown to be minimax optimal (up to some log terms) in Sobolev classes. Finally, we apply our algorithm on the simulated and real biological data.     

Comparing the effectiveness of respondent-driven sampling and quota sampling in migration research

We evaluate the effectiveness in empirical migration research of the respondent-driven sampling (RDS) and the quota sampling with regard to four criteria: quality of the data; sociometric diversity of the sample; geographic coverage of the sample; and cost-effectiveness. We review two surveys of ex-USSR migrants, conducted simultaneously in the Warsaw metropolitan area, each undertaken with the use of one of the two sampling methods. We find that although the RDS was considerably costlier, it enabled faster data collection and posed a lower risk of underestimating the cost of the survey. We also find that the RDS generated higher-quality data, and that it was more successful in surveying short-term circular migrants. This comparative evaluation contributes to the literature on social research methods in that it links the sampling methods with the quality and character of the data, and helps assess the usefulness and applicability of RDS in empirical migration studies.     

Double and group acceptance sampling plan for truncated life test based on inverse log-logistic distribution

This paper introduces a double and group acceptance sampling plans based on time truncated lifetimes when the lifetime of an item follows the inverse log-logistic (ILL) distribution with known shape parameter. The operating characteristic function and average sample number (ASN) values of the double acceptance sampling inspection plan are provided. The values of the minimum number of groups and operating characteristic function for various quality levels are obtained for a group acceptance sampling inspection plan. A comparative study between single acceptance sampling inspection plan and double acceptance sampling inspection plan is carried out in terms of sample size. One simulated example and four real-life examples are discussed to show the applicability of the proposed double and group acceptance sampling inspection plans for ILL distributed quality parameters.     

On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process

Data envelopment analysis (DEA) is a deterministic econometric model for calculating efficiency by using data from an observed set of decision-making units (DMUs). We propose a method for calculating the distribution of efficiency scores. Our framework relies on estimating data from an unobserved set of DMUs. The model provides posterior predictive data for the unobserved DMUs to augment the frontier in the DEA that provides a posterior predictive distribution for the efficiency scores. We explore the method on a multiple-input and multiple-output DEA model. The data for the example are from a comprehensive examination of how nursing homes complete a standardized mandatory assessment of residents.     

Reliability acceptance sampling plans for the Weibull distribution under accelerated Type-I censoring

Type-I censored reliability acceptance sampling plans (RASPs) are developed for the Weibull lifetime distribution with unknown shape and scale parameters such that the producer and consumer risks are satisfied. It is assumed that the life test is conducted at an accelerated condition for which the acceleration factor (AF) is known, and each item is continuously monitored for failure. Sensitivity analyses are also conducted to assess the effect of the uncertainty in the assumed AF on the actual producer and consumer risks, and a method is developed for constructing RASPs that can accommodate the uncertainty in AF.     

Modified inference about the mean of the exponential distribution using moving extreme ranked set sampling

The maximum likelihood estimator (MLE) and the likelihood ratio test (LRT) will be considered for making inference about the scale parameter of the exponential distribution in case of moving extreme ranked set sampling (MERSS). The MLE and LRT can not be written in closed form. Therefore, a modification of the MLE using the technique suggested by Maharota and Nanda (Biometrika 61:601–606, 1974) will be considered and this modified estimator will be used to modify the LRT to get a test in closed form for testing a simple hypothesis against one sided alternatives. The same idea will be used to modify the most powerful test (MPT) for testing a simple hypothesis versus a simple hypothesis to get a test in closed form for testing a simple hypothesis against one sided alternatives. Then it appears that the modified estimator is a good competitor of the MLE and the modified tests are good competitors of the LRT using MERSS and simple random sampling (SRS).     

Shrinkage inverse regression estimation for model-free variable selection

Summary.  The family of inverse regression estimators that was recently proposed by Cook and Ni has proven effective in dimension reduction by transforming the high dimensional predictor vector to its low dimensional projections. We propose a general shrinkage estimation strategy for the entire inverse regression estimation family that is capable of simultaneous dimension reduction and variable selection. We demonstrate that the new estimators achieve consistency in variable selection without requiring any traditional model, meanwhile retaining the root n estimation consistency of the dimension reduction basis. We also show the effectiveness of the new estimators through both simulation and real data analysis.     

Use of online crowdsourcing platforms for gambling research

Crowdsourcing platforms like Amazon’s Mechnical Turk and Crowdflower have been touted to be a cost-effective way to collect large amounts of behavioural data. Across four large-n studies, gambling-related behaviours, tendencies and traits among participants in these labour markets were examined. In Studies 1 and 2, both conducted on Crowdflower, problem gamblers (as measured by the benchmark Problem Gambling Severity Index) comprised 24.5% and 21.9% of participants, respectively. In Study 3, conducted on Mechanical Turk, problem gamblers comprised 9.0% of participants. In Study 4, a two-wave longitudinal study conducted on Crowdflower, problem gamblers comprised 13.5% of participants in wave one and 14.8% of participants in wave two. In Studies 2 and 3, strong convergent associations were demonstrated across various measures of problem gambling tendencies and general gambling involvement. Furthermore, it was demonstrated that gambling was associated with personality traits (impulsivity, sensation-seeking, self-control), risk attitudes, affect, and behavioural risk-taking consistent with previous research. In Study 4, it was demonstrated that measures of problem gambling have acceptable test-retest reliability. Online crowdsourcing platforms appear to offer access to samples with remarkably high proportions of problem gamblers. However, this characteristic means that such samples are not necessarily representative of gambling tendencies among more general populations.     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.