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This article focuses on the inference on the errors in manufactured parts controlled by using measurements devices. The characterization of the part surface topographies is core in several applications. A broad set of properties (tribological, optical, biological, mechanical, etc.) depends on the micro‐ and macrogeometry of the parts. Moreover, parts usually show typical deterministic geometric deviation pattern, referred to as manufacturing signatures, due to the specific manufacturing processes and process setup parameters adopted for their production. In several situations, the measurements may also be affected by systematic errors due to the measurement process, that might be caused, for example, by a poor part alignment during the measurement process. Measurement techniques and characterization methods have been standardized in the International Standard ISO 25178, defining parameters characterizing the surface topography and supplying methods and formula adapt to deal with this issue computationally. In the present article, we consider a type of spatial dependence between measured values at different points that suggest the use of the variogram to identify patterns in the parts. We offer a comparison, based on a real set of measures, between the latter approach and the conventional as a test of the efficient performance of our findings. [ABSTRACT FROM AUTHOR]

When sampling independent observations drawn from the uniform distribution on the unit interval, as the sample size gets large the asymptotic behaviour of both the empirical distribution function and empirical quantile function is well known. In this article we study analogous asymptotic results for the function that is obtained by composing the empirical quantile function with the empirical distribution function. Since the former is the generalized inverse of the latter, the result will approximate the identity function. We define a scaled and centered version of this function—the empirical identity process—and prove it converges to a highly irregular limit process whose trajectories are not right‐continuous and impossible to study using standard probability in metric spaces. However, when this process is integrated over time, and appropriately rescaled and centered, it becomes possible to define a functional limit theorem for it, which then converges to a randomly pinned Brownian motion. By applying these theoretical results, a new goodness‐of‐fit test is derived. We demonstrate that this test is very efficient when it is applied to data which come from a multimodal or mixture distribution, like the classic Old Faithful dataset. The Canadian Journal of Statistics 46: 656–672; 2018 © 2018 Société statistique du Canada [ABSTRACT FROM AUTHOR]

Non-stochastic simulation models, such as finite element or computational fluid dynamics, often support real experiments in industrial research. It has become a common practice to provide a meta-model as computer experiments can be highly complex and time-consuming, and the design space is often broad. The meta-model is an approximation of the computer experiments response adapted both globally and locally on the design space, in order to capture local minima/maxima. The Kriging model, first proposed in Geostatistics, is doubtlessly the most popular meta-model because of its recognized ability to provide high-quality predictions. The underlying correlation structure can be evaluated either by estimating the parameters of correlation or by means of a variogram. In this paper, the performance of the Kriging model is compared with an Artificial Neural Network meta-model in order to determine which model guarantees higher accuracy in predicting the result of four-dimensional computational fluid dynamics experiments for low pressure turbines where energy loss values are provided. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the production process of silicon wafers, which are crystalline slices used as substrate of electronic micro-circuits, the thickness of the SiO2 deposition on their top is a main characteristic to be controlled during the process. The experimental design that is commonly used to monitor the thickness to the target value consists of a regular array of points lying on concentric circles, the silicon wafer itself being a disk. To speed up the control process, the engineers aim to use just only a limited subset of such points. To reconstruct the values on untried locations of the silicon wafer, the Kriging interpolation has been proposed because of its recognized ability in providing fairly good predictions. In this paper, we consider two methodological issues related to universal Kriging models. First, we discuss the modeling of the covariance structure among the measured points; in fact, spatial data usually show a strong correlation when they come from spatially near observed points. Second, we put forward an algebraic method to assess the identifiability of trend models, based both on the full experimental design and on special fractions of it. Our findings are illustrated by a data set from an industrial application. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Tourism is a complex and highly competitive sector. In this scenario, incoming tourism flows represent one of the key indicators for public institutions, willing to adopt an informed decision-making process for resource allocation. The accurate and timely knowledge of both the inter-regional and the foreign component at a sufficiently detailed geographical level of such flows allows a better strategical, tactical and operational planning of marketing activities. The paper describes a methodology to complete the database of the official statistical data on tourism flows with an estimate of missing data, which are originated by non-respondent accommodation structure. The methodology has been applied to the inbound tourism flows into Piemonte, Italy. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Rheumatoid arthritis, anti-cyclic citrullinated peptide positivity, and cardiovascular disease risk in the Women's Health Initiative" by R.H. Mackey and others in a 2015 issue.