ENBIS-17 in Naples9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017
The following abstracts have been accepted for this event:
BivRegBLS: A New R Package in Method Comparison Studies with Tolerance Intervals and (Correlated)-Errors-in-Variables Regressions
Authors: Bernard Francq (GSK), Marion Berger (Sanofi)
Primary area of focus / application: Metrology & measurement systems analysis
Secondary area of focus / application: Quality
Keywords: Method comparison studies, Tolerance intervals, Bland-Altman, R, Errors-in-variables regressions, BivRegBLS
Submitted at 6-Mar-2017 23:55 by Bernard Francq
Two main methodologies are presented in the literature. The first one is the Bland–Altman approach with its agreement intervals (AIs) in a (M=(X+Y)/2,D=Y-1) space, where two methods (X and Y) are interchangeable if their differences are not clinically significant. The second approach is based on errors-in-variables regression in a classical (X,Y) plot, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors.
A consistent correlated-errors-in-variables (CEIV) regression is introduced as the errors are shown to be correlated in the Bland–Altman plot. The coverage probabilities collapse drastically and the biases soar when this correlation is ignored. Robust novel tolerance intervals (based on unreplicated or replicated designs) are shown to be better than AIs, and novel predictive intervals in the (X,Y) plot and in the (M,D) plot are introduced with excellent coverage probabilities.
Guidelines for practitioners will be discussed and illustrated with the new and promising R package BivRegBLS. It will be explained how to model and plot the data in the (X,Y) space with the BLS regression (Bivariate Least Square) or in the (M,D) space with the CBLS regression (Correlated-BLS) by using BivRegBLS. The main functions will be explored with an emphasis on the output and how to plot the results.
Advanced Run-to-Run Controller in Semiconductor Manufacturing with Real-time Equipment Condition
Authors: Wei-Ting Yang (École des Mines de Saint-Étienne), Jakey Blue (École des Mines de Saint-Étienne), Agnès Roussy (École des Mines de Saint-Étienne), Marco Reis (University of Coimbra)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Advanced Process Control (APC), Fault Detection and Classification (FDC), Principal Component Analysis (PCA), Canonical Correlation
Submitted at 7-Mar-2017 15:09 by Wei-Ting Yang
In this research, in order to extract more potential factors and exploit data more fully, the equipment state is assessed by leveraging the Fault Detection and Classification (FDC) data. It is an opportunity to analyze whether the FDC parameters that play critical roles can be explicitly used in the R2R model, i.e., if the generated regulations should take advantage of the existing FDC readings. Furthermore, since FDC data come with higher resolution (wafer-based multivariate temporal profile) while the metrology is constrained to the sampling strategy, FDC may provide more comprehensive information.
In this talk, we aligned the three types of data sources and report on the relational model that is under development. With the goal of finding the critical FDC signals or the composite indicators, we link the R2R and the metrology via the equipment behavior and validate the original path of R2R regulation purely based on the process physics.
Association Rules and Compositional Data Analysis: Implications to Big Data
Authors: Ron S. Kenett (KPA Ltd. and Neaman Institute), J.A. Martín-Fernández (University of Girona), S. Thió-Henestrosa (University of Girona), M. Vives-Mestres (University of Girona)
Primary area of focus / application: Other: Categorical data
Keywords: Association Rules (AR), Itemsets, Relative linkage disequilibrium (RLD), Compositional Data (CoDa), Subcompositional coherence, Big Data, Categorical data
Submitted at 8-Mar-2017 09:37 by Ron Kenett
Accepted (view paper)
The Choice of Screening Design
Authors: Muhammad Azam Chaudhry (Norwegian University of Science and Technology, Norway), John Sølve Tyssedal (Norwegian University of Science and Technology, Norway)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Design and analysis of experiments
Keywords: Factor screening, Definitive screening design, Minimum run resolution IV design, Plackett-Burman design
Submitted at 8-Mar-2017 10:16 by Muhammad Azam Chaudhry
Constrained Functional Time Series: An Application to Demand and Supply Curves in the Italian Natural Gas Balancing Platform
Authors: Antonio canale (Università di Padova)
Primary area of focus / application: Other: ENBIS Young Statistician Award
Keywords: Autoregressive model, Demand and offer model, Energy forecasting, Functional data analysis, Functional ridge regression
Submitted at 9-Mar-2017 09:54 by Antonio canale
Motivated by market dynamic modelling in the Italian natural gas balancing platform, we propose a model to analyze time series of monotone functions subject to an equality and inequality constraint at the two edges of the domain, respectively, such as daily demand and offer curves. In detail, we provide the constrained functions with a suitable pre-Hilbert structure and introduce a useful isometric bijective map associating each possible bounded and monotonic function to an unconstrained one. We introduce a functional-to-functional autoregressive model that is used to forecast future demand/offer functions. We estimate the model via minimization of a penalized mean squared error of prediction with a penalty term based on the Hilbert-Schmidt squared norm of autoregressive lagged operators.
The approach is of general interest and is suited for generalization in any situation in which one has to deal with functions subject to the above constraints which evolve through time.
Modelling Degradation Data Using the Gamma Process and Its Generalizations
Authors: Massimiliano Giorgio (Università della Campania "Luigi Vanvitelli"), Gianpaolo Pulcini (Istituto Motori, National Research Council (CNR))
Primary area of focus / application: Other: Statistical analysis of industrial reliability and maintenance data
Keywords: Degradation, Gamma process, Transformed Gamma process, Covariates, Random effect, Residual lifetime prediction
Submitted at 9-Mar-2017 17:52 by massimiliano Giorgio
Among the degradation processes proposed in the literature, the Gamma process is probably the widest applied in the reliability field. Its key success factors are flexibility and mathematical tractability, which allow dealing with many different kinds of degradation phenomena with a very limited computational burden. Relying on the latter feature, many authors have proposed generalizations of the gamma process, to use in specific practical settings. The aim of this paper is to survey some of these generalizations. In particular, we will focus on models that incorporate covariates, models that can account for the presence of random effects, models that assume the dependence of the degradation increment on the current degradation level (or state) of the units, and combinations thereof. All these models will be presented and examples of applications to real degradation data will be illustrated.