Sample Splitting and Assessing Goodness-of-fit of Time Series

A fundamental and often final step in time series modeling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are inherently dependent since they are based on the same parameter estimates and thus standard tests of serial independence, such as those based on the autocorrelation function (ACF) or distance correlation function (ADCF) of the fitted residuals need to be adjusted. The sample splitting procedure in Pfister et al.~(2018) is one such fix for the case of models for independent data, but fails to work in the dependent setting. In this paper sample splitting is leveraged in the time series setting to perform tests of serial dependence of fitted residuals using the ACF and ADCF. Here the first $f_n$ of the data points are used to estimate the parameters of the model and then using these parameter estimates, the last $l_n$ of the data points are used to compute the estimated residuals. Tests for serial independence are then based on these $l_n$ residuals. As long as the overlap between the $f_n$ and $l_n$ data splits is asymptotically 1/2, the ACF and ADCF tests of serial independence tests often have the same limit distributions as though the underlying residuals are indeed iid. In particular if the first half of the data is used to estimate the parameters and the estimated residuals are computed for the entire data set based on these parameter estimates, then the ACF and ADCF can have the same limit distributions as though the residuals were iid. This procedure ameliorates the need for adjustment in the construction of confidence bounds for both the ACF and ADCF in goodness-of-fit testing.Comment: 31 pages, 4 figures, 1 tabl

Clustering Multivariate Time Series using Energy Distance

A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Sz\'ekely and Rizzo (2013). Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure separation between the finite dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering method is then applied to obtain the dendrogram. This procedure is completely nonparametric as the dissimilarities between stationary distributions are directly calculated without making any model assumptions. In order to justify this procedure, asymptotic properties of the energy distance estimates are derived for general stationary and ergodic time series. The method is illustrated in a simulation study for various component time series that are either linear or nonlinear. Finally the methodology is applied to two examples; one involves GDP of selected countries and the other is population size of various states in the U.S.A. in the years 1900 -1999.Comment: 26 pages, 7 figures, to be published in Journal of Time Series Anaylsi

Application of Distance Covariance to Time Series Modeling and Assessing Goodness-of-Fit

The overarching goal of this thesis is to use distance covariance based methods to extend asymptotic results from the i.i.d. case to general time series settings. Accounting for dependence may make already difficult statistical inference all the more challenging. The distance covariance is an increasingly popular measure of dependence between random vectors that goes beyond linear dependence as described by correlation. It is defined by a squared integral norm of the difference between the joint and marginal characteristic functions with respect to a specific weight function. Distance covariance has the advantage of being able to detect dependence even for uncorrelated data. The energy distance is a closely related quantity that measures distance between distributions of random vectors. These statistics can be used to establish asymptotic limit theory for stationary ergodic time series. The asymptotic results are driven by the limit theory for the empirical characteristic functions. In this thesis we apply the distance covariance to three problems in time series modeling: (i) Independent Component Analysis (ICA), (ii) multivariate time series clustering, and (iii) goodness-of-fit using residuals from a fitted model. The underlying statistical procedures for each topic uses the distance covariance function as a measure of dependence. The distance covariance arises in various ways in each of these topics; one as a measure of independence among the components of a vector, second as a measure of similarity of joint distributions and, third for assessing serial dependence among the fitted residuals. In each of these cases, limit theory is established for the corresponding empirical distance covariance statistics when the data comes from a stationary ergodic time series. For Topic (i) we consider an ICA framework, which is a popular tool used for blind source separation and has found application in fields such as financial time series, signal processing, feature extraction, and brain imaging. The Structural Vector Autogregression (SVAR) model is often the basic model used for modeling macro time series. The residuals in such a model are given by e_t = A S_t, the classical ICA model. In certain applications, one of the components of S_t has infinite variance. This differs from the standard ICA model. Furthermore the e_t's are not observed directly but are only estimated from the SVAR modeling. Many of the ICA procedures require the existence of a finite second or even fourth moment. We derive consistency when using the distance covariance for measuring independence of residuals under the infinite variance case.Extensions to the ICA model with noise, which has a direct application to SVAR models when testing independence of residuals based on their estimated counterparts is also considered. In Topic (ii) we propose a novel methodology for clustering multivariate time series data using energy distance. Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure separation between the finite dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering method is then applied to obtain the dendrogram. This procedure is completely nonparametric as the dissimilarities between stationary distributions are directly calculated without making any model assumptions. In order to justify this procedure, asymptotic properties of the energy distance estimates are derived for general stationary and ergodic time series. Topic (iii) considers the fundamental and often final step in time series modeling, assessing the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are inherently dependent since they are based on the same parameter estimates and thus standard tests of serial independence, such as those based on the autocorrelation function (ACF) or distance correlation function (ADCF) of the fitted residuals need to be adjusted. We apply sample splitting in the time series setting to perform tests of serial dependence of fitted residuals using the sample ACF and ADCF. Here the first f_n of the n data points in the time series are used to estimate the parameters of the model. Tests for serial independence are then based on all the n residuals. With f_n = n/2 the ACF and ADCF tests of serial independence tests often have the same limit distributions as though the underlying residuals are indeed i.i.d. That is, if the first half of the data is used to estimate the parameters and the estimated residuals are computed for the entire data set based on these parameter estimates, then the ACF and ADCF can have the same limit distributions as though the residuals were i.i.d. This procedure ameliorates the need for adjustment in the construction of confidence bounds for both the ACF and ADCF, based on the fitted residuals, in goodness-of-fit testing. We also show that if f_n n/2

Development of a phage-based biosensor to detect salmonella in food stuff

info:eu-repo/semantics/publishedVersio

Development of an Oxy-Fuel Combustion System in a Compression-Ignition Engine for Ultra-Low Emissions Powerplants Using CFD and Evolutionary Algorithms

[EN] This study uses an optimization approach for developing a combustion system in a compression-ignition engine that is able to operate under oxy-fuel conditions, and produces mainly CO2 and H2O as exhaust gases. This is achieved because the combustion concept uses pure oxygen as an oxidizer, instead of air, avoiding the presence of nitrogen. The O-2 for the combustion system can be obtained by using a mixed ionic-electronic conducting membrane (MIEC), which separates the oxygen from the air onboard. The optimization method employed maximizes the energy conversion of the system, reducing pollutant emissions (CxHy, particulate matter, and carbon monoxides) to levels near zero. The methodology follows a novel approach that couples computational fluid dynamics (CFD) and particle swarm optimization (PSO) algorithms to optimize the complete combustion system in terms of engine performance and pollutant generation. The study involves the evaluation of several inputs that govern the combustion system design in order to fulfill the thermo-mechanical constraints. The parameters analyzed are the piston bowl geometry, fuel injector characteristics, air motion, and engine settings variables. Results evince the relevance of the optimization procedure, achieving very low levels of gaseous pollutants (CxHy and CO) in the optimum configuration. The emissions of CO were reduced by more than 10% while maintaining the maximum in-cylinder pressure within the limit imposed for the engine. However, indicated efficiency levels are compromised if they are compared with an equivalent condition operating under conventional diesel combustion.This research work has been supported by Grant PDC2021-120821-I00 funded by MCIN/AEI/10.13039/501100011033 and by EuropeanUnion NextGenerationEU/PRTR. This research was partially supported by Agencia Valenciana de la Innovacio (AVI) through the project "Demostrador de un motor de oxicombustion con captura de CO2" (INNVA1/2021/38).Serrano, J.; Bracho Leon, G.; Gómez-Soriano, J.; Spohr-Fernandes, C. (2022). Development of an Oxy-Fuel Combustion System in a Compression-Ignition Engine for Ultra-Low Emissions Powerplants Using CFD and Evolutionary Algorithms. Applied Sciences. 12(14):1-27. https://doi.org/10.3390/app12147104127121

Spirometric Assessment of Lung Transplant Patients: One Year Follow-Up

OBJECTIVE: The purpose of this study was to compare spirometry data between patients who underwent single-lung or double-lung transplantation the first year after transplantation. INTRODUCTION: Lung transplantation, which was initially described as an experimental method in 1963, has become a therapeutic option for patients with advanced pulmonary diseases due to improvements in organ conservation, surgical technique, immunosuppressive therapy and treatment of post-operative infections. METHODS: We retrospectively reviewed the records of the 39 patients who received lung transplantation in our institution between August 2003 and August 2006. Twenty-nine patients survived one year post-transplantation, and all of them were followed. RESULTS: The increase in lung function in the double-lung transplant group was more substantial than that of the single-lung transplant group, exhibiting a statistical difference from the 1st month in both the forced expiratory volume in one second (FEV1) and the forced vital capacity (FVC) in comparison to the pre-transplant values (p <0.05). Comparison between double-lung transplant and single lung-transplant groups of emphysema patients demonstrated a significant difference in lung function beginning in the 3rd month after transplantation. DISCUSSION: The analyses of the whole group of transplant recipients and the sub-group of emphysema patients suggest the superiority of bilateral transplant over the unilateral alternative. Although the pre-transplant values of lung function were worse in the double-lung group, this difference was no longer significant in the subsequent months after surgery. CONCLUSION: Although both groups demonstrated functional improvement after transplantation, there was a clear tendency to greater improvement in FVC and FEV1 in the bilateral transplant group. Among our subjects, double-lung transplantation improved lung function

The AMIGA sample of isolated galaxies. XI. Optical characterisation of nuclear activity

Context.- This paper is part of a series involving the AMIGA project (Analysis of the Interstellar Medium of Isolated GAlaxies), which identifies and studies a statistically-significant sample of the most isolated galaxies in the northern sky. Aims.- We present a catalogue of nuclear activity, traced by optical emission lines, in a well-defined sample of the most isolated galaxies in the local Universe, which will be used as a basis for studying the effect of the environment on nuclear activity. Methods.- We obtained spectral data from the 6th Data Release of the Sloan Digital Sky Survey, which were inspected in a semi-automatic way. We subtracted the underlying stellar populations from the spectra (using the software Starlight) and modelled the nuclear emission features. Standard emission-line diagnostics diagrams were applied, using a new classification scheme that takes into account censored data, to classify the type of nuclear emission. Results.- We provide a final catalogue of spectroscopic data, stellar populations, emission lines and classification of optical nuclear activity for AMIGA galaxies. The prevalence of optical active galactic nuclei (AGN) in AMIGA galaxies is 20.4%, or 36.7% including transition objects. The fraction of AGN increases steeply towards earlier morphological types and higher luminosities. We compare these results with a matched analysis of galaxies in isolated denser environments (Hickson Compact Groups). After correcting for the effects of the morphology and luminosity, we find that there is no evidence for a difference in the prevalence of AGN between isolated and compact group galaxies, and we discuss the implications of this result. Conclusions.- We find that a major interaction is not a necessary condition for the triggering of optical AGN.Comment: 16 pages, 11 figures, 12 tables, published in Astronomy and Astrophysics. Figure 5 corrected: [OI] diagram adde

Transforming ideas and developing entrepreneurship skills in computing sciences and informatics engineering courses

This paper presents an approach on entrepreneurship education which helps to turn ideas into Minimum Viable Products (MVP) and to capacitate students to become entrepreneurs. In this approach, we integrate development and management project to different business models. Students acquire, in addition to technical competencies, skills on market knowledge and business modeling. This approach has been applied for several years in an informatics engineering course and suggests a set of activities on 18 weeks. Teachers’ perceptions and students’ opinions were collected through direct observations and using a questionnaire in order to evaluate the process behind this pedagogical project which goes beyond the walls of the university. Most of the students are satisfied with the process since they develop projects that have a good fit with the market needs and opportunities and some of them are close to creating a startup.(undefined

Photosynthesis, yield and raw material quality of sugarcane injured by multiple pests

Understanding sugarcane (Saccharum spp.) response to multiple pest injury, sugarcane borer (Diatraea saccharalis) and spittlebug (Mahanarva fimbriolata), is essential to make better management decisions. Moreover, the consequences of both pests on the sugarcane raw material quality have not yet been studied. A field experiment was performed in São Paulo State, Brazil, where sugarcane plants were exposed to pests individually or in combination. Plots consisted of a 2-m long row of caged sugarcane plants. Photosynthesis was measured once every 3 months (seasonal measurement). Yield and sugar production were assessed. The measured photosynthesis rate was negatively affected by both borer and spittlebug infestations. Photosynthesis reduction was similar on plants infested by both pests as well as by spittlebug individual infestation. Plants under spittlebug infestation resulted in yield losses and represented 17.6% (individual infestation) and 15.5% (multiple infestations). The sucrose content and the sucrose yield per area were reduced when plants were infested by multiple pests or spittlebug