965 research outputs found
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Feature extraction and dimensionality reduction are important tasks in many
fields of science dealing with signal processing and analysis. The relevance of
these techniques is increasing as current sensory devices are developed with
ever higher resolution, and problems involving multimodal data sources become
more common. A plethora of feature extraction methods are available in the
literature collectively grouped under the field of Multivariate Analysis (MVA).
This paper provides a uniform treatment of several methods: Principal Component
Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis
(CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions
derived by means of the theory of reproducing kernel Hilbert spaces. We also
review their connections to other methods for classification and statistical
dependence estimation, and introduce some recent developments to deal with the
extreme cases of large-scale and low-sized problems. To illustrate the wide
applicability of these methods in both classification and regression problems,
we analyze their performance in a benchmark of publicly available data sets,
and pay special attention to specific real applications involving audio
processing for music genre prediction and hyperspectral satellite images for
Earth and climate monitoring
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Density forecasts of inflation using Gaussian process regression models
The present study uses Gaussian Process regression models for generating density forecasts of inflation within the New Keynesian Phillips curve (NKPC) framework. The NKPC is a structural model of inflation dynamics in which we include the output gap, inflation expectations, fuel world prices and money market interest rates as predictors. We estimate country-specific time series models for the 19 Euro Area (EA) countries. As opposed to other machine learning models, Gaussian Process regression allows estimating confidence intervals for the predictions. The performance of the proposed model is assessed in a one-step-ahead forecasting exercise. The results obtained point out the recent inflationary pressures and show the potential of Gaussian Process regression for forecasting purposes
Supervised and Ensemble Classification of Multivariate Functional Data: Applications to Lupus Diagnosis
abstract: This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.Dissertation/ThesisDoctoral Dissertation Applied Mathematics 201
Explaining the Negative Coefficient Associated with Human Capital in Augmented Solow Growth Regressions
In this paper we consider different explanations for why the coefficient associated with human capital is often negative in growth regressions once country-specific effects are controlled for whereas the coefficient in question is strongly positive in cross-sectional or panel results based on the pooling estimator. In turn, we explore: (i) additional sources of unobserved heterogeneity stemming from country-specific rates of labor-augmenting technological change, (ii) measurement error in the human capital series being used, and (iii) the lack of variability in the human capital series once the usual covariance transformations are implemented. Remaining unobserved country-specific heterogeneity and measurement error alone are shown to be inadequate explanations. The lack of variability in the human capital series is tackled using a new GMM-based estimator that combines the Hausman-Taylor (1981) approach, in which the impact of time-invariant covariates can be identified through use of covariance transformations of the variables themselves as instruments, with the orthogonality conditions of the Arellano-Bond (1991) estimator.panel estimation, measurement error, human capital, economic growth
The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond
Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases
Large-Scale Galaxy Bias
This review presents a comprehensive overview of galaxy bias, that is, the
statistical relation between the distribution of galaxies and matter. We focus
on large scales where cosmic density fields are quasi-linear. On these scales,
the clustering of galaxies can be described by a perturbative bias expansion,
and the complicated physics of galaxy formation is absorbed by a finite set of
coefficients of the expansion, called bias parameters. The review begins with a
detailed derivation of this very important result, which forms the basis of the
rigorous perturbative description of galaxy clustering, under the assumptions
of General Relativity and Gaussian, adiabatic initial conditions. Key
components of the bias expansion are all leading local gravitational
observables, which include the matter density but also tidal fields and their
time derivatives. We hence expand the definition of local bias to encompass all
these contributions. This derivation is followed by a presentation of the
peak-background split in its general form, which elucidates the physical
meaning of the bias parameters, and a detailed description of the connection
between bias parameters and galaxy statistics. We then review the excursion-set
formalism and peak theory which provide predictions for the values of the bias
parameters. In the remainder of the review, we consider the generalizations of
galaxy bias required in the presence of various types of cosmological physics
that go beyond pressureless matter with adiabatic, Gaussian initial conditions:
primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature
perturbations, dark energy, and modified gravity. Finally, we discuss how the
description of galaxy bias in the galaxies' rest frame is related to clustering
statistics measured from the observed angular positions and redshifts in actual
galaxy catalogs.Comment: 259 pages, 39 figures, 15 tables; published in Physics Reports; v2:
minor corrections and clarifications, references added; v3: substantially
revised and improved version; v4: minor edits and clarifications reflecting
published version, corrected mistakes in Eqs. (7.57)-(7.58); v5: minor
corrections [Eq. (5.5)] and updated reference
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