Telephone surveys under different frame constructions—evaluating differences in estimates,precision, and cost

Dual-frame, random-digit-dialing (DFRDD) surveys, using a frame of landline telephone numbers and a frame of cell phone numbers, have become the norm for telephone surveys. Two alternative frame constructions sample exclusively from a cell phone frame, referred to as the ‘single-frame cell,’ and sample from the listed landline frame and the cell phone frame, referred to as ‘listed/cell.’ The listed/cell frame construction differs from the DFRDD in that it does not select unlisted-landline frame members. We observe in our simulated data that the listed/cell frame construction results in estimates that more closely approximate the estimates from DFRDD compared with the single-frame cell, results in an improvement in precision compared with the DFRDD that is not as large as the improvement in the single-frame cell, and has lower cost than both the DFRDD and the single-frame cell. We conclude that a listed/cell frame construction might be a better solution than either a DFRDD or single-frame cell frame construction.     

Understanding peasant revolution: From concept to theory and case

Theory and Society -     

Risky social choice with incomplete or noisy interpersonal comparisons of well-being

In this study, we develop a model of social choice over lotteries, where people’s psychological characteristics are mutable, their preferences may be incomplete, and incomplete or noisy interpersonal comparisons of well-being are possible. Formally, we suppose individual preferences are described by a von Neumann-Morgenstern (vNM) preference order on a space of lotteries over psychophysical states; the social planner must construct a vNM preference order on lotteries over social states. First, we consider a model where the individual vNM preference order is incomplete (so not all interpersonal comparisons are possible). Then, we consider a model where the individual vNM preference order is complete, but unknown to the planner, and thus modelled by a random variable. In both cases, we obtain characterizations of a utilitarian social welfare function.     

Time Use Choices and Volunteer Labour Supply

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This paper examines time allocation and satisfaction with time use of volunteers and non-volunteers. Using data from the...     

Variable Selection in Linear Mixed Models Using an Extended Class of Penalties

There is an emerging need to advance linear mixed model technology to include variable selection methods that can simultaneously choose and estimate important effects from a potentially large number of covariates. However, the complex nature of variable selection has made it difficult for it to be incorporated into mixed models. In this paper we extend the well known class of penalties and show that they can be integrated succinctly into a linear mixed model setting. Under mild conditions, the estimator obtained from this mixed model penalised likelihood is shown to be consistent and asymptotically normally distributed. A simulation study reveals that the extended family of penalties achieves varying degrees of estimator shrinkage depending on the value of one of its parameters. The simulation study also shows there is a link between the number of false positives detected and the number of true coefficients when using the same penalty. This new mixed model variable selection (MMVS) technology was applied to a complex wheat quality data set to determine significant quantitative trait loci (QTL).     

Bayesian variable selection in quantile regression with random effects: an application to Municipal Human Development Index

According to the Atlas of Human Development in Brazil, the income dimension of Municipal Human Development Index (MHDI-I) is an indicator that shows the population''s ability in a municipality to ensure a minimum standard of living to provide their basic needs, such as water, food and shelter. In public policy, one of the research objectives is to identify social and economic variables that are associated with this index. Due to the income inequality, evaluate these associations in quantiles, instead of the mean, could be more interest. Thus, in this paper, we develop a Bayesian variable selection in quantile regression models with hierarchical random effects. In particular, we assume a likelihood function based on the Generalized Asymmetric Laplace distribution, and a spike-and-slab prior is used to perform variable selection. The Generalized Asymmetric Laplace distribution is a more general alternative than the Asymmetric Laplace one, which is a common approach used in quantile regression under the Bayesian paradigm. The performance of the proposed method is evaluated via a comprehensive simulation study, and it is applied to the MHDI-I from municipalities located in the state of Rio de Janeiro.     

Univariate fast initial response statistical process control with taut strings

We present a novel real-time univariate monitoring scheme for detecting a sustained departure of a process mean from some given standard assuming a constant variance. Our proposed stopping rule is based on the total variation of a nonparametric taut string estimator of the process mean and is designed to provide a desired average run length for an in-control situation. Compared to the more prominent CUSUM fast initial response (FIR) methodology and allowing for a restart following a false alarm, the proposed two-sided taut string (TS) scheme produces a significant reduction in average run length for a wide range of changes in the mean that occur at or immediately after process monitoring begins. A decision rule for when to choose our proposed TS chart compared to the CUSUM FIR chart that takes into account both false alarm rate and average run length to detect a shift in the mean is proposed and implemented. Supplementary materials are available online.