Search

This paper considers new techniques for complex surveys in the case of estimation of proportions when the variable of interest has ordinal outcomes. Ordinal modelassisted and ordinal model-calibrated estimators are introduced for class frequencies in a population, taking two different approaches. Theoretical properties and numerical methods are investigated. Simulation studies using data from a real macro survey are considered to evaluate the performance of the proposed estimators. The empirical coverage and the length of confidence intervals are computed using several techniques in variance estimation. We also use data from an opinion survey to show the behavior of the proposed estimators in real applications.

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

This paper reports the use of FEA (Finite Element Analysis) to model dinosaur tracks. Satisfactory reproductions of sauropod ichnites were simulated using 3D numerical models of the elasto-plastic behaviour of soils. Though the modelling was done of ichnites in situ at the Miraflores I tracksite (Soria, Spain), the methodology could be applied to other tracksites to improve their ichnological interpretation and better understand how the type and state of the trodden sediment at the moment the track is created is a fundamental determinant of the morphology of the ichnite. The results obtained explain why the initial and commonly adopted hypothesis—that soft sediments become progressively more rigid and resistant at depth—is not appropriate at this tracksite. We explain why it is essential to consider a more rigid superficial layer (caused by desiccation) overlying a softer layer that is extruded to form a displacement rim. Adult sauropods left trackways behind them. These tracks could be filled up with water due to phreatic level was close to the ground surface. The simulation provides us with a means to explain the differences between similar tracks (of different depths; with or without displacement rims) in the various stratigraphic layers of the tracksite and to explain why temporary and variable conditions of humidity lead to these differences in the tracks. The simulations also demonstrate that track depth alone is insufficient to differentiate true tracks from undertracks and that other discrimination criteria need to be taken into account. The scarcity of baby sauropod tracks is explained because they are shallow and easily eroded.

Although there is already a great amount of scientific literature dealing with the use of building information modeling (BIM) in engineering activities, the majority refer to successful case studies using the usual methods and technology of building construction but rarely bring up the real problems for implementing BIM methodology to the field of transportation infrastructure. It must be also considered that the construction activity is only a part of the infrastructure life and the stakeholder must consider the works of enlargement, renewal, and maintenance of the infrastructure. The purpose of this paper is not only to show a mere review of the existing literature but also present a rational analysis for the use of BIM in different areas of civil engineering. For that purpose, the gathered experience in the use of BIM in civil engineering projects in the final course of Civil Engineering Master Studies in the Civil Engineering School (ETSICCP) at Universidad Politécnica de Madrid were compared with the reported literature. This way, a complete and updated information regarding tendencies, applications, and practice along with limitations and benefits can be presented. The significance of this research relies on the original insight of BIM for civil engineering applications through four case studies. Two of them were focused on construction possibilities and the other two on the possibilities in the exploitation, rehabilitation, and maintenance. The results showed that despite the lack of previous experiences, the use of BIM methodology is possible for activities such as maintenance, managing, or expansion of infrastructure by applying different specific software packages. Among the main problems needing to be addressed are the following: handling of big data files, the integration of new data non-related with the modeled object, and interchange of data without losing information. That proves the need of new more efficient techniques to overcome the challenge of the full use of BIM in the civil engineering field and obtain the mutual advantage of the co-operation of the academic and industrial worlds.

The use of probability-based panels that collect data via online or mixed-mode surveys has increased in the last few years as an answer to the growing concern with the quality of the data obtained with traditional survey modes. However, in order to adequately represent the general population, these tools must address the same sources of bias that affect other survey-based designs: namely under coverage and non-response. In this work, we test several approaches to produce calibration estimators that are suitable for survey data affected by non response where auxiliary information exists at both the panel level and the population level. The first approach adjusts the results obtained in the cross-sectional survey to the population totals, while, in the second, the weights are the result of two-step process where different adjusts on the sample, panel, and population are done. A simulation on the properties of these estimators is performed. In light of theory and simulation results, we conclude that weighting by calibration is an effective technique for the treatment of non-response bias when the response mechanism is missing at random. These techniques have also been applied to real data from the survey Andalusian Citizen Preferences for Political Decision-Making Procedures.

This paper considers the problem of estimating the population proportion of a categorical variable using the calibration framework. Different situations are explored according to the level of auxiliary information available and the theoretical properties are investigated. A new class of estimator based upon the proposed calibration estimators is also defined, and the optimal estimator in the class, in the sense of minimal variance, is derived. Finally, an estimator of the population proportion, under new calibration conditions, is defined. Simulation studies are considered to evaluate the performance of the proposed calibration estimators via the empirical relative bias and the empirical relative efficiency, and favourable results are achieved.