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A new strategy for tripling
Level permutations of factors can improve space-filling properties of designs, and the properties of the three-level Triple designs constructed by...
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Hierarchical disjoint principal component analysis
Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest...
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Multiple doubling: a simple effective construction technique for optimal two-level experimental designs
Design of experiment is an efficient statistical methodology of establishing which input variables are important (have significant effects) in an...
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Efficiency Bound Under Identifiability Constraints in Semiparametric Models
The purpose of this work is to define an adequate efficiency bound in some models presenting some identification problems. We show how it is possible...
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Local projections for high-dimensional outlier detection
A novel approach for outlier detection is proposed, called local projections, which is based on concepts of the Local Outlier Factor (LOF) (Breunig...
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Scale invariant and efficient estimation for groupwise scaled envelope model
Motivated by different groups containing different group information under the heteroscedastic error structure, we propose the groupwise scaled...
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MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Map** Models
Exploring spatial patterns in the context of disease map** is a decisive approach to bring evidence of geographical tendencies in assessing disease...
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Connections to Statistical Inference and Epistemic Probabilities
This chapter deals with theoretical issues that certainly can be more extensively discussed. The mathematics of the previous chapters is further...
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Uniformity pattern of q-level factorials under mixture discrepancy
The objective of this paper is to discuss the issue of the projection uniformity of factorial designs measured by mixture discrepancy. The average...
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Finite mixtures, projection pursuit and tensor rank: a triangulation
Finite mixtures of multivariate distributions play a fundamental role in model-based clustering. However, they pose several problems, especially in...
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Robust signal dimension estimation via SURE
The estimation of signal dimension under heavy-tailed latent variable models is studied. As a primary contribution, robust extensions of an earlier...
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Minimum spectral connectivity projection pursuit
We study the problem of determining the optimal low-dimensional projection for maximising the separability of a binary partition of an unlabelled...
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Estimation
This brief chapter introduces least squares estimation in a linear model, in particular the error sum of squares, and studies the basic distribution...
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A Random-Coefficients Analysis with a Multivariate Random-Coefficients Linear Model
Random-coefficients linear models can be considered as a particular case of linear mixed models. Different sources of variation are treated by random...
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Fractional Factorial Designs
This chapter asks how much of the decompositions of a full factorial design can be recovered when we observe only a subset of all treatment...
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Data integration via analysis of subspaces (DIVAS)
Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e.,...
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Multinomial Principal Component Logistic Regression on Shape Data
This paper proposes a linear model that uses the principal component scores in shape data and fits the nominal responses in the tangent space of...
