Robust Ordinal Embedding from Contaminated Relative Comparisons

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.Comment: Accepted by AAAI 201

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

This study was supported by the “Programa de Desarrollo de Ciencias Básicas”, PEDECIBA; Agencia Nacional de investigación e innovación (ANII), (FCE_1_2017_1_136550) and the “Comisión Sectorial de Investigación Científica” (CSIC) I + D - 2016 - 589 grant from Uruguay. N.R. acknowledges the CSIC group grant “CSIC2018 - FID 13 - Grupo ID 722”.Peer reviewedPublisher PD

Symbolic transfer entropy test for causality in longitudinal data

In this paper, we use multiple-unit symbolic dynamics and the concept of transfer entropy to develop a non-parametric Granger causality test procedure for longitudinal data. Monte Carlo simulations show that our test displays the correct size and large power in situations where linear panel data causality tests fail such as when the linearity assumption breaks down, when the data generating process is heterogeneous across the cross-section units or presents struc-tural breaks, when there are extreme observations in some of the cross-section units, when the process displays causal dependence in the conditional variance and when the analysis involves qualitative data. We illustrate the usefulness of our proposal with the analysis of the dynamic causal relationships between public expenditure and GDP, between rm productivity and rm size in US manufacturing sectors, and among sovereign credit rating, growth and interest rates.The authors acknowledge financial support from protect PID2019-107192 GB-I00 (AEI/10.13039/501100011033) and from MINECO projects ECO2016-76178-P and ECO2015-65637-P, which are co-financed by FEDER funds. This study is part of the collaborative activities performed under the program Groups of Excellence of the Region of Murcia, the Fundacion Seneca, Science and Technology Agency of the Region of Murcia Project 19884/GERM/15

Symbolic transfer entropy test for causality in longitudinal data

© 2020 Elsevier B.V.. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the accepted version of a published work that appeared in final form in Economic Modelling.In this study, we use multiple-unit symbolic dynamics and transfer entropy to develop a non-parametric Granger causality test procedure for longitudinal data. Monte Carlo simulations show that our test exhibits the correct size and a high power in situations where linear panel data causality tests fail, such as (1) when the linearity assumption does not hold, (2) when the data generating process is heterogeneous across the cross-section units or presents structural breaks, (3) when there are extreme observations in some of the cross-section units, (4) when the process exhibits causal dependence on the conditional variance, or (5) when the analysis involves qualitative data. We illustrate the usefulness of our proposed procedure by analyzing the dynamic causal relationships between public expenditure and GDP, between firm productivity and firm size in US manufacturing sectors, and among sovereign credit ratings, growth, and interest rates

Generalized Low Rank Models

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, $k$-means, $k$-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.Comment: 84 pages, 19 figure

Frame-of-reference bias in subjective welfare regressions

Past research has found that subjective questions about an individuals'economic status do not correspond closely to measures of economic welfare based on household income or consumption. Survey respondents undoubtedly hold diverse ideas about what it means to be"poor"or"rich."Further, this heterogeneity may be correlated with other characteristics, including welfare, leading to frame-of-reference bias. To test for this bias, vignettes were added to a nationally representative survey of Tajikistan, in which survey respondents rank the economic status of the theoretical vignette households, as well as their own. The vignette rankings are used to reveal the respondent's own scale. The findings indicate that respondents hold diverse scales in assessing their welfare, but that there is little bias in either the economic gradient of subjective welfare or most other coefficients on covariates of interest. These results provide a firmer foundation for standard survey methods and regression specifications for subjective welfare data.Rural Poverty Reduction,Housing&Human Habitats,Economic Theory&Research,Poverty Lines,Agricultural Knowledge&Information Systems