In the literatures on public opinion and legislative behavior, there are debates over (1) how constrained preferences are and (2) whether they are captured by a single left–right spectrum or require multiple dimensions. But insufficient formalization has led scholars to equate a lack of constraint with multidimensional preferences. In this paper, we refine the concepts of constraint and dimensionality in a formal framework and describe how they translate into separate observable implications for political preferences. We use this discussion to motivate a cross-validation estimator that measures constraint and dimensionality in the context of canonical ideal point models. Using data from the public and politicians, we find that American political preferences are one-dimensional, but there is more constraint among politicians than among the mass public. Furthermore, we show that differences between politicians and the public are not explained by differences in agendas or the incentives faced by the actors.

The aim of this study was to examine the factorial validity of the Job Expectations Questionnaire (Cuestionario de Expectativas Laborales CEL) in a sample of Mexican workers. Following a cross validation approach, two samples were used in the study. The first sample consisted of 380 professionals who mainly performed administrative work in the Health Services in Puebla-Mexico. The second sample comprised 400 health professionals from the Hospital de la Mujer in Puebla-Mexico. Exploratory factor analysis yielded a three-factor solution, accounting for 51.8% of the variance. The results of confirmatory factorial analysis indicate that the three-factor model provided the best fit with the data (CFI = .96, GFI = .95, NNFI = .95, RMSEA = .04), maintaining the structure with 12 items. The reliability of the questionnaire and the diverse subscales showed high internal consistency. Significant correlations were found between job expectations and autonomy, vigor, dedication, and absorption, providing evidence of its construct validity. The evaluation of the psychometric qualities confirms this questionnaire as a valid and specific instrument to measure job expectations.

Assume that (Xt )t∈Z is a real valued time seriesadmitting a common marginal density f with respect to Lebesgue's measure. [Donoho et al. Ann. Stat.24 (1996) 508–539] propose near-minimax estimators $\widehat f_n$ based on thresholding wavelets to estimate f on a compact set in an independent and identically distributed setting. The aim of the present work is to extend these results to general weak dependent contexts. Weak dependence assumptions are expressed as decreasing bounds of covariance terms and are detailed for different examples. The threshold levels in estimators $\widehat f_n$ depend on weak dependence properties of the sequence (Xt )t∈Z through the constant. If these properties are unknown, we propose cross-validation procedures to get new estimators. These procedures are illustrated via simulations of dynamical systems and non causal infinite moving averages. We also discuss the efficiency of our estimators with respect to the decrease of covariances bounds.

Blue sharks (Prionace glauca) and oilfish (Ruvettus pretiosus) are frequently caught incidentally in the swordfish surface drifting longline fishery of the eastern Mediterranean Sea. An information theoretic generalized additive model approach, modelling separately: (1) the probability of making a catch; and (2) the positive catch rates, suggested the presence of intra-annual variations in their abundance and revealed interesting associations with some environmental features. Blue sharks were more frequently encountered during late summer in cool water masses. However, the likelihood of making a larger catch peaked in warmer waters through late spring in the vicinity of land. Occurrences of blue sharks were more frequent to the south-west (Ionian Sea), while positive catch rates were higher in the eastern-most regions (Levantine). Oilfish occurrences, as well as catch rates, were always above average to the east. Intra-annual variation was analogous to that of blue sharks. The species showed a preference for cooler water bodies and its distribution was strongly dictated by the lunar cycle. Annual time series showed a decreasing trend of blue sharks abundance. Catch rates for both species depended on fishing gear configuration. Deeper settings (>20 m), more resilient lines and use of fish attractant light-sticks increased the probability of capturing a fish.

Traits such as disease resistance are costly to evaluate and slow to improve using current methods. Analysis of gene expression profiles (e.g. DNA microarrays) has potential for predicting such phenotypes and has been used in an analogous way to classify cancer types in human patients. However, doubts have been raised regarding the use of classification methods with microarray data for this purpose. Here we propose a method using random regression with cross validation, which accounts for the distribution of variation in the trait and utilises different subsets of patients or animals to perform a complete validation of predictive ability. Published breast tumour data were used to test the method. Despite the small dataset (n < 100), the new approach resulted in a moderate but significant correlation between the predicted and actual phenotypes (0.32). Binary classification of the predicted phenotypes yielded similar classification error rates to those found by other authors (35%). Unlike other methods, the new method gave a quantitative estimate of phenotype that could be used to rank animals and select those with extreme phenotypic performance. Use of the method in an optimal way using larger sample sizes, and combining DNA microarrays and other testing platforms, is recommended.

While the traditional R 2 value is useful to evaluate the quality of a fit, it does not work when it comes to evaluating the predictive power of estimated financial models in finite samples. In this paper we introduce a validated value useful for prediction. Based on data from the Danish stock market, using this measure we find that the dividend-price ratio has predictive power. The best horizon for prediction seems to be four years. On a one year horizon, we find that while inflation and interest rate do not add to the predictive power of the dividend-price ratio then last years excess stock return does.

This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.