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Aledo, J. A., Goles, E., Montalva-Medel, M., Montealegre, P., & Valverde, J. C. (2023). Symmetrizable Boolean networks. Inf. Sci., 626, 787–804.
Abstract: In this work, we provide a procedure that allows us to transform certain kinds of deterministic Boolean networks on minterm or maxterm functions into symmetric ones, so inferring that such symmetrizable networks can present only periodic points of periods 1 or 2. In particular, we deal with generalized parallel (or synchronous) dynamical systems (GPDS) over undirected graphs, i. e., discrete parallel dynamical systems over undirected graphs where some of the self-loops may not appear. We also study the class of anti-symmetric GPDS (which are non-symmetrizable), proving that their periodic orbits have period 4. In addition, we introduce a class of non-symmetrizable systems which admit periodic orbits with arbitrary large periods.
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Bachoc, F., Porcu, E., Bevilacqua, M., Furrer, R., & Faouzi, T. (2022). Asymptotically equivalent prediction in multivariate geostatistics. Bernoulli, 28(4), 2518–2545.
Abstract: Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geo-statistics. While best linear prediction has been well understood in univariate spatial statistics, the literature for the multivariate case has been elusive so far. The new challenges provided by modern spatial datasets, being typ-ically multivariate, call for a deeper study of cokriging. In particular, we deal with the problem of misspecified cokriging prediction within the framework of fixed domain asymptotics. Specifically, we provide conditions for equivalence of measures associated with multivariate Gaussian random fields, with index set in a compact set of a d-dimensional Euclidean space. Such conditions have been elusive for over about 50 years of spatial statistics. We then focus on the multivariate Matern and Generalized Wendland classes of matrix valued covariance functions, that have been very popular for having parameters that are crucial to spatial interpolation, and that control the mean square differentiability of the associated Gaussian process. We provide sufficient conditions, for equivalence of Gaussian measures, relying on the covariance parameters of these two classes. This enables to identify the parameters that are crucial to asymptotically equivalent interpolation in multivariate geostatistics. Our findings are then illustrated through simulation studies.
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Balbontin, C., Hensher, D. A., & Ho, C. (2023). Light commercial vehicles destination choice: Understanding preferences relative to the number of stop and tour-based trip type. Transp. Res. E-Logist. Transp. Rev., 171, 103042.
Abstract: Freight delivery modelling has made significant progress in the past few decades. In this study we propose to use an aggregate multi-step approach to gain a better understanding of the tour-based trips of light commercial vehicles in Sydney, Australia. The paper identifies differences in destination choice-making given by the number of stop and the stop count of the trip, defined by the total number of stops in the tour-based trip. The findings suggest that estimating a separate model for each number of stops and stop count provides a better understanding on how desti-nation choices are made. Different scenarios were simulated to show how the probability of choosing a certain destination depending on the number of stop and stop count changes due to variations in travel time and distance. Results show that light commercial vehicles are more sensitive to the generalised cost (defined by travel time and distance) in the first stop, and the sensitivity decreases as the trip is completed.
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Bevilacqua, M., Camano-Carrillo, C., & Porcu, E. (2022). Unifying compactly supported and Matern covariance functions in spatial statistics. J. Multivar. Anal., 189, 104949.
Abstract: The Matern family of covariance functions has played a central role in spatial statistics for decades, being a flexible parametric class with one parameter determining the smoothness of the paths of the underlying spatial field. This paper proposes a family of spatial covariance functions, which stems from a reparameterization of the generalized Wendland family. As for the Matern case, the proposed family allows for a continuous parameterization of the smoothness of the underlying Gaussian random field, being additionally compactly supported.
More importantly, we show that the proposed covariance family generalizes the Matern model which is attained as a special limit case. This implies that the (reparametrized) Generalized Wendland model is more flexible than the Matern model with an extra-parameter that allows for switching from compactly to globally supported covariance functions. Our numerical experiments elucidate the speed of convergence of the proposed model to the Matern model. We also inspect the asymptotic distribution of the maximum likelihood method when estimating the parameters of the proposed covariance models under both increasing and fixed domain asymptotics. The effectiveness of our proposal is illustrated by analyzing a georeferenced dataset of mean temperatures over a region of French, and performing a re-analysis of a large spatial point referenced dataset of yearly total precipitation anomalies. |
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Cominetti, R., Quattropani, M., & Scarsini, M. (2022). The Buck-Passing Game. Math. Oper. Res., Early Access.
Abstract: We consider two classes of games in which players are the vertices of a directed graph. Initially, nature chooses one player according to some fixed distribution and gives the player a buck. This player passes the buck to one of the player's out-neighbors in the graph. The procedure is repeated indefinitely. In one class of games, each player wants to minimize the asymptotic expected frequency of times that the player receives the buck. In the other class of games, the player wants to maximize it. The PageRank game is a particular case of these maximizing games. We consider deterministic and stochastic versions of the game, depending on how players select the neighbor to which to pass the buck. In both cases, we prove the existence of pure equilibria that do not depend on the initial distribution; this is achieved by showing the existence of a generalized ordinal potential. If the graph on which the game is played admits a Hamiltonian cycle, then this is the outcome of prior-five Nash equilibrium in the minimizing game. For the minimizing game, we then use the price of anarchy and stability to measure fairness of these equilibria.
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Elorrieta, F., Eyheramendy, S., & Palma, W. (2019). Discrete-time autoregressive model for unequally spaced time-series observations. Astron. Astrophys., 627, 11 pp.
Abstract: Most time-series models assume that the data come from observations that are equally spaced in time. However, this assumption does not hold in many diverse scientific fields, such as astronomy, finance, and climatology, among others. There are some techniques that fit unequally spaced time series, such as the continuous-time autoregressive moving average (CARMA) processes. These models are defined as the solution of a stochastic differential equation. It is not uncommon in astronomical time series, that the time gaps between observations are large. Therefore, an alternative suitable approach to modeling astronomical time series with large gaps between observations should be based on the solution of a difference equation of a discrete process. In this work we propose a novel model to fit irregular time series called the complex irregular autoregressive (CIAR) model that is represented directly as a discrete-time process. We show that the model is weakly stationary and that it can be represented as a state-space system, allowing efficient maximum likelihood estimation based on the Kalman recursions. Furthermore, we show via Monte Carlo simulations that the finite sample performance of the parameter estimation is accurate. The proposed methodology is applied to light curves from periodic variable stars, illustrating how the model can be implemented to detect poor adjustment of the harmonic model. This can occur when the period has not been accurately estimated or when the variable stars are multiperiodic. Last, we show how the CIAR model, through its state space representation, allows unobserved measurements to be forecast.
Keywords: methods: statistical; methods: data analysis; stars: general
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Elorrieta, F., Eyheramendy, S., Palma, W., & Ojeda, C. (2021). A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series. Mon. Not. Roy. Astron. Soc., 505(1), 1105–1116.
Abstract: In several disciplines, it is common to find time series measured at irregular observational times. In particular, in astronomy there are a large number of surveys that gather information over irregular time gaps and in more than one passband. Some examples are Pan-STARRS, ZTF, and also the LSST. However, current commonly used time series models that estimate the time dependence in astronomical light curves consider the information of each band separately (e.g, CIAR, IAR, and CARMA models) disregarding the dependence that might exist between different passbands. In this paper, we propose a novel bivariate model for irregularly sampled time series, called the Bivariate Irregular Autoregressive (BIAR) model. The BIAR model assumes an autoregressive structure on each time series; it is stationary, and it allows to estimate the autocorrelation, the cross-correlation and the contemporary correlation between two unequally spaced time series. We implemented the BIAR model on light curves, in the g and r bands, obtained from the ZTF alerts processed by the ALeRCE broker. We show that if the light curves of the two bands are highly correlated, the model has more accurate forecast and prediction using the bivariate model than a similar method that uses only univariate information. Further, the estimated parameters of the BIAR are useful to characterize long-period variable stars and to distinguish between classes of stochastic objects, providing promising features that can be used for classification purposes.
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Faouzi, T., Porcu, E., & Bevilacqua, M. (2022). SPACE-TIME ESTIMATION AND PREDICTION UNDER FIXED-DOMAIN ASYMPTOTICS WITH COMPACTLY SUPPORTED COVARIANCE FUNCTIONS. Stat. Sin., 32(3), 1187–1203.
Abstract: We study the estimation and prediction of Gaussian processes with spacetime covariance models belonging to the dynamical generalized Wendland (DGW) family, under fixed-domain asymptotics. Such a class is nonseparable, has dynamical compact supports, and parameterizes differentiability at the origin similarly to the space-time Matern class.
Our results are presented in two parts. First, we establish the strong consistency and asymptotic normality for the maximum likelihood estimator of the microergodic parameter associated with the DGW covariance model, under fixed-domain asymptotics. The second part focuses on optimal kriging prediction under the DGW model and an asymptotically correct estimation of the mean squared error using a misspecified model. Our theoretical results are, in turn, based on the equivalence of Gaussian measures under some given families of space-time covariance functions, where both space or time are compact. The technical results are provided in the online Supplementary material. |
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Fernandez, M., Munoz, F. D., & Moreno, R. (2020). Analysis of imperfect competition in natural gas supply contracts for electric power generation: A closed-loop approach. Energy Econ., 87, 15 pp.
Abstract: The supply of natural gas is generally based on contracts that are signed prior to the use of this fuel for power generation. Scarcity of natural gas in systems where a share of electricity demand is supplied with gas turbines does not necessarily imply demand rationing, because most gas turbines can still operate with diesel when natural gas is not available. However, scarcity conditions can lead to electricity price spikes, with welfare effects for consumers and generation firms. We develop a closed-loop equilibrium model to evaluate if generation firms have incentives to contract or import the socially-optimal volumes of natural gas to generate electricity. We consider a perfectly-competitive electricity market, where all firms act as price-takers in the short term, but assume that only a small number of firms own gas turbines and procure natural gas from, for instance, foreign suppliers in liquefied form. We illustrate an application of our model using a network reduction of the electric power system in Chile, considering two strategic firms that make annual decisions about natural gas imports in discrete quantities. We also assume that strategic firms compete in the electricity market with a set of competitive firms do not make strategic decisions about natural gas imports (i.e., a competitive fringe). Our results indicate that strategic firms could have incentives to sign natural gas contracts for volumes that are much lower than the socially-optimal ones, which leads to supernormal profits for these firms in the electricity market. Yet, this effect is rather sensitive to the price of natural gas. A high price of natural gas eliminates the incentives of generation firms to exercise market power through natural gas contracts. (C) 2020 Elsevier B.V. All rights reserved.
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Kalahasthi, L. K., Sutar, P., Yushimito, W. F., & Holguin-Veras, J. (2023). Optimal Sampling Plan for Freight Demand Synthesis with Mode Choice: A Case study of Bangladesh. Transp. Res. Record, Early Access.
Abstract: This paper uses a comprehensive experimental design to investigate the influence of various traffic count sampling plans for Bangladesh on the performance of the Freight Origin-Destination Synthesis model with Mode Choice (FODS-MC) developed by Kalahasthi et al. FODS-MC estimates a national-level freight demand model including trip distribution, mode choice, empty truck trips, and empty rail trips, where one of the key inputs is the freight truck and rail, traffic counts. The traffic count sample comprises three types of road links (national, regional, and zilla) and one category for the rail link across the country. A Box-Behnken Design (BBD) with a response surface for each of four FODS-MC parameters (distribution, mode choice, truck empty trips, and rail empty trips) is constructed. The results showed that the response surfaces are nonlinear planes for all parameters. There is no single optimal sampling plan for FODS-MC as each model parameter demands different distribution across the truck and rail links. The random and stratified samples perform almost similarly if less than 20% of the sample is collected. Minimizing the loss functions between the estimated and true parameters shows that a random sample between 20% and 25% of the truck and rail links estimates the best freight demand model. Overall, this research develops a framework to assist public practitioners in the optimum usage of the limited time and resources in collecting the traffic count data that could estimate the freight demand and mode choice models effectively.
Keywords: freight systems; general; freight model; modeling
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Leiva, V., Liu, S. Z., Shi, L., & Cysneiros, F. J. A. (2016). Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics. J. Appl. Stat., 43(4), 627–642.
Abstract: We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.
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Lillo, C., Leiva, V., Nicolis, O., & Aykroyd, R. G. (2018). L-moments of the Birnbaum-Saunders distribution and its extreme value version: estimation, goodness of fit and application to earthquake data. J. Appl. Stat., 45(2), 187–209.
Abstract: Understanding patterns in the frequency of extreme natural events, such as earthquakes, is important as it helps in the prediction of their future occurrence and hence provides better civil protection. Distributions describing these events are known to be heavy tailed and positive skew making standard distributions unsuitable for modelling the frequency of such events. The Birnbaum-Saunders distribution and its extreme value version have been widely studied and applied due to their attractive properties. We derive L-moment equations for these distributions and propose novel methods for parameter estimation, goodness-of-fit assessment and model selection. A simulation study is conducted to evaluate the performance of the L-moment estimators, which is compared to that of the maximum likelihood estimators, demonstrating the superiority of the proposed methods. To illustrate these methods in a practical application, a data analysis of real-world earthquake magnitudes, obtained from the global centroid moment tensor catalogue during 1962-2015, is carried out. This application identifies the extreme value Birnbaum-Saunders distribution as a better model than classic extreme value distributions for describing seismic events.
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Mahajan, S. M., Asenjo, F. A., & Hazeltine, R. D. (2015). Comparison of the electron-spin force and radiation reaction force. Mon. Not. Roy. Astron. Soc., 446(4), 4112–4115.
Abstract: It is shown that the forces that originate from the electron-spin interacting with the electromagnetic field can play, along with the Lorentz force, a fundamentally important role in determining the electron motion in a high energy density plasma embedded in strong high-frequency radiation, a situation that pertains to both laser-produced and astrophysical systems. These forces, for instance, dominate the standard radiation reaction force as long as there is a 'sufficiently' strong ambient magnetic field for affecting spin alignment. The inclusion of spin forces in any advanced modelling of electron dynamics pertaining to high energy density systems (for instance in particle-in-cell codes), therefore, is a must.
Keywords: magnetic fields; plasmas; radiation mechanisms: general
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Marchant, C., Leiva, V., & Cysneiros, F. J. A. (2016). A Multivariate Log-Linear Model for Birnbaum-Saunders Distributions. IEEE Trans. Reliab., 65(2), 816–827.
Abstract: Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate generalized Birnbaum-Saunders regression models. We use the maximum likelihood method and the EM algorithm to estimate their parameters. We carry out a simulation study to evaluate the performance of the corresponding maximum likelihood estimators. We illustrate the new models with real-world multivariate fatigue data.
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Qadir, A., Asenjo, F. A., & Mahajan, S. M. (2014). Magnetic field seed generation in plasmas around charged and rotating black holes. Phys. Scr., 89(8), 7 pp.
Abstract: Previous work by the authors introduced the possibility of generating seed magnetic fields by spacetime curvature and applied it in the vicinity of a Schwarzschild black hole. It was pointed out that it would be worthwhile to consider the effect in other background geometries and particularly in the vicinity of a rotating black hole, which is generically to be expected, astrophysically. In this paper that suggestion is followed up and we calculate generated magnetic field seed due to Reissner-Nordstrom and Kerr spacetimes. The conditions for the drive for the seed of a magnetic field is obtained for charged black holes, finding that in the horizon the drive vanishes. Also, the psi N-force produced by the Kerr black hole is obtained and its relation with the magnetic field seed is discussed, producing a more effective drive.
Keywords: seed magnetic field; general relativistic drive; Kerr metric
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Smith, A. M. S., Acton, J. S., Anderson, D. R., Armstrong, D. J., Bayliss, D., Belardi, C., et al. (2021). NGTS-14Ab: a Neptune-sized transiting planet in the desert. Astron. Astrophys., 646, A183.
Abstract: Context. The sub-Jovian, or Neptunian, desert is a previously identified region of parameter space where there is a relative dearth of intermediate-mass planets with short orbital periods.Aims. We present the discovery of a new transiting planetary system within the Neptunian desert, NGTS-14.Methods. Transits of NGTS-14Ab were discovered in photometry from the Next Generation Transit Survey (NGTS). Follow-up transit photometry was conducted from several ground-based facilities, as well as extracted from TESS full-frame images. We combine radial velocities from the HARPS spectrograph with the photometry in a global analysis to determine the system parameters.Results. NGTS-14Ab has a radius that is about 30 per cent larger than that of Neptune (0.444 +/- 0.030 R-Jup) and is around 70 per cent more massive than Neptune (0.092 +/- 0.012 M-Jup). It transits the main-sequence K1 star, NGTS-14A, with a period of 3.54 days, just far away enough to have maintained at least some of its primordial atmosphere. We have also identified a possible long-period stellar mass companion to the system, NGTS-14B, and we investigate the binarity of exoplanet host stars inside and outside the Neptunian desert using Gaia.
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