Quick counts from non-selected polling stations

Countless examples of misleading forecasts on behalf of both campaign and exit polls affecting, among others, British, French, and Spanish elections could be found. This has seriously damaged their image. Therefore, procedures should be used that minimize errors, especially on election night when errors are more noticeable, in order to maintain people's trust in surveys. This paper proposes a method to obtain quick and early outcome forecasts on the election night. The idea is to partly sample some (whatever) polling stations and use the consistency that polling stations show between elections to predict the final results. Model accuracy is analysed through simulation using seven different types of samples in four elections. The efficacy of the technique is also tested predicting the 2005 Eusko Legebiltzarra elections from real data. Results confirm that the procedure generates highly reliable and accurate forecasts. Furthermore, compared with the classical quick count strategy, the method is revealed as much more robust and precise.     

Some Restricted randomization rules in sequential designs

This paper presents a new class of designs (Big Stick Designs) for sequentially assigning experimental units to treatments, when only the time covariate is considered. By prescribing the degree of imbalance which the experimenters can tolerate, complete randomization is used as long as the imbalance of the treatment allocation does not exceed the prescribed value. Once it reaches the value, a deterministic assignment is made to lower the imbalance. Such designs can be easily implemented with no programming and little personnel support. They compare favorably with the Biased Coin Designs, the Permuted Black Designs, and the Urn Designs, as far as the accidental bias and selection bias are concerned. Generalizations of these designs are considered to achieve various purposes, e.g., avoidance of deterministic assignments, early balance, etc.     

Frame evolution: A new approach to understanding changes in diversity reforms at public universities in the United States

University diversity policy in the past two decades has shifted from rationales that focused on social justice to ones that emphasize educational excellence [Lipson, D. (2007, Fall). Embracing diversity: The institutionalization of affirmative action as diversity management at UC-Berkeley, UT-Austin, and UW-Madison. Law & Social Inquiry, 32(4), 985–1026; Moreno, J. F., Smith, D. G., Parker, S., Clayton-Pedersen, A. R., & Teraguchi, D. H. (2006). Using multiple lenses: An examination of the economic and racial/ethnic diversity of college students. Report from the James Irvine Foundation Campus Diversity Initiative; Moses, M. S., & Chang, M. J. (2006). Toward a deeper understanding of the diversity rationale. Educational Researcher, 35(1), 6–11; Skrenty, J. D. (2002). The minority rights revolution. Princeton University Press]. The consensus among diversity scholars is that this shift has taken place as a result of Federal court decisions restricting the use of affirmative action in admissions. This paper sheds new light on these shifts in rationale by proposing a new theory of frame evolution. I developed this, an extension to Snow et al.’s [Snow, D. A., et al. (1986). Frame alignment processes, micromobilization, and movement participation. American Sociological Review, 51(4), 464–481] theory of frame alignment, as a way to show how policy discourses evolve to accommodate changing demands. I argue that policy frames tend to evolve in a sequential pattern of accommodation, consolidation, and bridging. In-depth interview data with 41 diversity advocates at four public universities in the United States reveal three stages in the frame evolution process: the first motivated by a need to accommodate existing institutional norms, the second motivated by a need to build a core constituency of supporters, and the third with the aim of converting those “on the fence” into policy supporters. In the case of higher education diversity policy, the three stages equate to a maintenance frame, a social justice frame, and an excellence frame.     

Analysis of capital expenditure projects with uncertain discrete cash flows

In this paper, a methodology is presented for evaluating risky projects whose cash flows, start times and durations are not known with certainty. The methodology directly computes statistics of the present worth (i.e. means and variances) from closed-form analytical formulas which are derived with explicit consideration of the risks associated with the individual variables that affect the profitability index of the projects. It is assumed that the cash flows occur discretely over time and that a fixed discount rate exists over the project duration. The application of the technique is demonstrated through systematic steps on a number of example problems.     

Bayesian inference in a matrix normal dynamic linear model with unknown covariance matrices

In this paper, we consider the problem of estimating the parameters of a matrix normal dynamic linear model when the variance and covariance matrices of its error terms are unknown and can be changing over time. Given that the analysis is not conjugate, we use simulation methods based on Monte Carlo Markov chains to estimate the parameters of the model. This analysis allows us to carry out a dynamic principal components analysis in a set of multivariate time series. Furthermore, it permits the treatment of series with different lengths and with missing data. The methodology is illustrated with two empirical examples: the value added distribution of the firms operating in the manufacturing sector of the countries participating in the BACH project, and the joint evolution of a set of international stock-market indices.     

Characterizations and ordering properties based on log-odds functions

In this paper, we obtain some general results on characterizations of probability distributions from relationships between conditional moment, failure rate, and log-odds rate functions. We also study stochastic orders and classes based on the log-odds rate function and some relationships with usual stochastic orderings and classes. Some characterizations and ordering properties are obtained by using weighted distributions.     

A multiple group item response theory model with centered skew-normal latent trait distributions under a Bayesian framework

Very often, in psychometric research, as in educational assessment, it is necessary to analyze item response from clustered respondents. The multiple group item response theory (IRT) model proposed by Bock and Zimowski [12] provides a useful framework for analyzing such type of data. In this model, the selected groups of respondents are of specific interest such that group-specific population distributions need to be defined. The usual assumption for parameter estimation in this model, which is that the latent traits are random variables following different symmetric normal distributions, has been questioned in many works found in the IRT literature. Furthermore, when this assumption does not hold, misleading inference can result. In this paper, we consider that the latent traits for each group follow different skew-normal distributions, under the centered parameterization. We named it skew multiple group IRT model. This modeling extends the works of Azevedo et al. [4], Bazán et al. [11] and Bock and Zimowski [12] (concerning the latent trait distribution). Our approach ensures that the model is identifiable. We propose and compare, concerning convergence issues, two Monte Carlo Markov Chain (MCMC) algorithms for parameter estimation. A simulation study was performed in order to evaluate parameter recovery for the proposed model and the selected algorithm concerning convergence issues. Results reveal that the proposed algorithm recovers properly all model parameters. Furthermore, we analyzed a real data set which presents asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of negative asymmetry for some latent trait distributions.