Electricity consumption prediction with functional linear regression using spline estimators

A functional linear regression model linking observations of a functional response variable with measurements of an explanatory functional variable is considered. This model serves to analyse a real data set describing electricity consumption in Sardinia. The interest lies in predicting either oncoming weekends’ or oncoming weekdays’ consumption, provided actual weekdays’ consumption is known. A B-spline estimator of the functional parameter is used. Selected computational issues are addressed as well.     

Rivalry and strategic groups: what makes a company a rival?

Strategic group literature has generated a significant amount of research over recent decades. However, the rivalry implications of strategic group have remained unclear. This paper analyses rivalry and strategic groups in the house building industry in a small town from a cognitive approach. We consider rivalry as a subjective and directional phenomenon. Estimating rivalry as the direct identification of competitors we try to explain whether similarity affects rivalry and what factors make a company a “rival”. Results show that perceived rivalry is strongly related to size, past performance, subjective similarity and strategic group structure.

Therapeutic residential care in Spain. Population treated and therapeutic coverage

Therapeutic residential care (TRC) is the name given to specialized children's homes for treating cases with severe emotional and behavioural problems that have been placed in residential care. A recent international review has revealed great diversity in the referral criteria of cases and in the models of intervention carried out. The goal of this study is to describe the population treated in these types of facilities in Spain and the therapeutic coverage given. The sample is made up of 215 young people in children's homes, of whom 93 are in TRC. The cases referred to TRC have been in residential care for less time but have gone through a greater number of placements. These young people also exhibit more problems of drug use, and there is a larger percentage of clinical‐range cases in the Child Behaviour Checklist scales of attention problems and aggressive behaviour. Nevertheless, the results of logistic regression indicate that the only variables that significantly increase the probability of being referred to TRC are drug use and changes of care placements. With regard to therapeutic care, there is a higher percentage in the TRC group receiving psychiatric care, and the sessions are also more frequent.     

The inverse power Lindley distribution in the presence of left-censored data

In this study, classical and Bayesian inference methods are introduced to analyze lifetime data sets in the presence of left censoring considering two generalizations of the Lindley distribution: a first generalization proposed by Ghitany et al. [Power Lindley distribution and associated inference, Comput. Statist. Data Anal. 64 (2013), pp. 20–33], denoted as a power Lindley distribution and a second generalization proposed by Sharma et al. [The inverse Lindley distribution: A stress–strength reliability model with application to head and neck cancer data, J. Ind. Prod. Eng. 32 (2015), pp. 162–173], denoted as an inverse Lindley distribution. In our approach, we have used a distribution obtained from these two generalizations denoted as an inverse power Lindley distribution. A numerical illustration is presented considering a dataset of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.     

A reweighting approach to robust clustering

An iteratively reweighted approach for robust clustering is presented in this work. The method is initialized with a very robust clustering partition based on an high trimming level. The initial partition is then refined to reduce the number of wrongly discarded observations and substantially increase efficiency. Simulation studies and real data examples indicate that the final clustering solution has both good properties in terms of robustness and efficiency and naturally adapts to the true underlying contamination level.     

Advances in estimation by the item sum technique using auxiliary information in complex surveys

To collect sensitive data, survey statisticians have designed many strategies to reduce nonresponse rates and social desirability response bias. In recent years, the item count technique has gained considerable popularity and credibility as an alternative mode of indirect questioning survey, and several variants of this technique have been proposed as new needs and challenges arise. The item sum technique (IST), which was introduced by Chaudhuri and Christofides (Indirect questioning in sample surveys, Springer-Verlag, Berlin, 2013) and Trappmann et al. (J Surv Stat Methodol 2:58–77, 2014), is one such variant, used to estimate the mean of a sensitive quantitative variable. In this approach, sampled units are asked to respond to a two-list of items containing a sensitive question related to the study variable and various innocuous, nonsensitive, questions. To the best of our knowledge, very few theoretical and applied papers have addressed the IST. In this article, therefore, we present certain methodological advances as a contribution to appraising the use of the IST in real-world surveys. In particular, we employ a generic sampling design to examine the problem of how to improve the estimates of the sensitive mean when auxiliary information on the population under study is available and is used at the design and estimation stages. A Horvitz–Thompson-type estimator and a calibration-type estimator are proposed and their efficiency is evaluated by means of an extensive simulation study. Using simulation experiments, we show that estimates obtained by the IST are nearly equivalent to those obtained using “true data” and that in general they outperform the estimates provided by a competitive randomized response method. Moreover, variance estimation may be considered satisfactory. These results open up new perspectives for academics, researchers and survey practitioners and could justify the use of the IST as a valid alternative to traditional direct questioning survey modes.     

A new way of quantifying the symmetry of a random variable: Estimation and hypothesis testing

New measures of skewness for real-valued random variables are proposed. The measures are based on a functional representation of real-valued random variables. Specifically, the expected value of the transformed random variable can be used to characterize the distribution of the original variable. Firstly, estimators of the proposed skewness measures are analyzed. Secondly, asymptotic tests for symmetry are developed. The tests are consistent for both discrete and continuous distributions. Bootstrap versions improving the empirical results for moderated and small samples are provided. Some simulations illustrate the performance of the tests in comparison to other methods. The results show that our procedures are competitive and have some practical advantages.