8,859 research outputs found
Possibilities of using graphical and numerical tools in the exposition of process capability assessment techniques
Purpose: The paper focuses on how the problem of process capability assessment can be handled when taught, using convenient numerical and graphical means. The contents of the paper results from the authors' own academic and practical experience, which suggested that many important steps are overlooked in the process of selecting and using capability indices.
Methodology/Approach: Selected problems in capability assessment are illustrated with suitable examples and graphs.
Findings: The authors' experience is reflected in the paper, aiming to emphasize what matters and how, and what does not. Also, a new capability index is introduced.
Research Limitation/implication: The style in which the problems are analysed may serve as a guide for further studies in the field and capability index applications.
Originality/Value of paper: The paper also contains, aside from specific examples, some more advanced techniques, and is therefore accompanied by software readouts, since computer support is required in such cases.
Category: Conceptual paperWeb of Science232331
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Traditional linear methods for forecasting multivariate time series are not
able to satisfactorily model the non-linear dependencies that may exist in
non-Gaussian series. We build on the theory of learning vector-valued functions
in the reproducing kernel Hilbert space and develop a method for learning
prediction functions that accommodate such non-linearities. The method not only
learns the predictive function but also the matrix-valued kernel underlying the
function search space directly from the data. Our approach is based on learning
multiple matrix-valued kernels, each of those composed of a set of input
kernels and a set of output kernels learned in the cone of positive
semi-definite matrices. In addition to superior predictive performance in the
presence of strong non-linearities, our method also recovers the hidden dynamic
relationships between the series and thus is a new alternative to existing
graphical Granger techniques.Comment: Accepted for ECML-PKDD 201
Asymmetric multivariate normal mixture GARCH
An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is shown that the disaggregation of the conditional (co)variance process generated by the model provides substantial intuition. Moreover, the model exhibits a strong performance in calculating outāofāsample ValueāatāRisk measures
Asymptotic Signal Detection Rates with 1-bit Array Measurements
This work considers detecting the presence of a band-limited random radio
source using an antenna array featuring a low-complexity digitization process
with single-bit output resolution. In contrast to high-resolution
analog-to-digital conversion, such a direct transformation of the analog radio
measurements to a binary representation can be implemented hardware and
energy-efficient. However, the probabilistic model of the binary receive data
becomes challenging. Therefore, we first consider the Neyman-Pearson test
within generic exponential families and derive the associated analytic
detection rate expressions. Then we use a specific replacement model for the
binary likelihood and study the achievable detection performance with 1- bit
radio array measurements. As an application, we explore the capability of a
low-complexity GPS spectrum monitoring system with different numbers of
antennas and different observation intervals. Results show that with a moderate
amount of binary sensors it is possible to reliably perform the monitoring
task
Multivariate Statistical Process Control Charts: An Overview
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
Progressive surface modeling scheme from unorganised curves
This paper presents a novel surface modelling scheme to construct a freeform surface
progressively from unorganised curves representing the boundary and interior characteristic curves.
The approach can construct a base surface model from four ordinary or composite boundary curves
and support incremental surface updating from interior characteristic curves, some of which may not
be on the final surface. The base surface is first constructed as a regular Coons surface and upon receiving an interior curve sketch, it is then updated. With this progressive modelling scheme, a final
surface with multiple sub-surfaces can be obtained from a set of unorganised curves and transferred
to commercial surface modelling software for detailed modification. The approach has been tested
with examples based on 3D motion sketches; it is capable of dealing with unorganised design curves
for surface modelling in conceptual design. Its limitations have been discussed
tolerance: An R Package for Estimating Tolerance Intervals
The tolerance package for R provides a set of functions for estimating and plotting tolerance limits. This package provides a wide-range of functions for estimating discrete and continuous tolerance intervals as well as for estimating regression tolerance intervals. An additional tool of the tolerance package is the plotting capability for the univariate and regression settings as well as for the multivariate normal setting. The tolerance package's capabilities are illustrated using simulated data sets. Formulas used for the estimation procedures are also presented.
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