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I completed my PhD in Statistics in 2001 under the supervision of Professor Feridum Turkman (University of Lisbon, Portugal) and Professor Clive W. Anderson (University of Sheffield, UK). My dissertation was on the extremes of certain transformations of time series. My research interests center in applied probability and sometimes cross the boundary into statistics. Current topics of research gravitate towards problems in integer-valued time series analysis, forecasting, mathematical economics, classification, extreme value theory and in applied statistics.
Although regulatory improvements for air quality in the European Union have been made, air pollution is still a pressing problem and, its impact on health, both mortality and morbidity, is a topic of intense research nowadays. The main goal of this work is to assess the impact of the exposure to air pollutants on the number of daily hospital admissions due to respiratory causes in 58 spatial locations of Portugal mainland, during the period 2005-2017. To this end, INteger Generalised AutoRegressive Conditional Heteroskedastic (INGARCH)-based models are extensively used. This family of models has proven to be very useful in the analysis of serially dependent count data. Such models include information on the past history of the time series, as well as the effect of external covariates. In particular, daily hospitalisation counts, air quality and temperature data are endowed within INGARCH models of optimal orders, where the automatic inclusion of the most significant covariates is carried out through a new block-forward procedure. The INGARCH approach is adequate to model the outcome variable (respiratory hospital admissions) and the covariates, which advocates for the use of count time series approaches in this setting. Results show that the past history of the count process carries very relevant information and that temperature is the most determinant covariate, among the analysed, for daily hospital respiratory admissions. It is important to stress that, despite the small variability explained by air quality, all models include on average, approximately two air pollutants covariates besides temperature. Further analysis shows that the one-step-ahead forecasts distributions are well separated into two clusters: one cluster includes locations exclusively in the Lisbon area (exhibiting higher number of one-step-ahead hospital admissions forecasts), while the other contains the remaining locations. This results highlights that special attention must be given to air quality in Lisbon metropolitan area in order to decrease the number of hospital admissions.
Ana Martins; Manuel Scotto; Ricardo Deus; Alexandra Monteiro; Sónia Gouveia. Association between respiratory hospital admissions and air quality in Portugal: A count time series approach. PLoS ONE 2021, 16, e0253455 .
AMA StyleAna Martins, Manuel Scotto, Ricardo Deus, Alexandra Monteiro, Sónia Gouveia. Association between respiratory hospital admissions and air quality in Portugal: A count time series approach. PLoS ONE. 2021; 16 (7):e0253455.
Chicago/Turabian StyleAna Martins; Manuel Scotto; Ricardo Deus; Alexandra Monteiro; Sónia Gouveia. 2021. "Association between respiratory hospital admissions and air quality in Portugal: A count time series approach." PLoS ONE 16, no. 7: e0253455.
In this paper we investigate extremal properties connected with the so-called max-INAR process of order one based on the binomial thinning operator, and marginal distribution exhibiting regularly-varying right-tail. In particular, attention is paid to the limiting distribution of the number of exceedances of high levels and the joint limiting law of the maximum and the minimum. Furthermore, we also look at the extremal behavior of the max-INAR process of order one under the assumption that its corresponding thinning parameter is random. Finally, the periodic case is also addressed.
Manuel G. Scotto; Sónia Gouveia. On the extremes of the max-INAR(1) process for time series of counts. Communications in Statistics - Theory and Methods 2021, 1 -19.
AMA StyleManuel G. Scotto, Sónia Gouveia. On the extremes of the max-INAR(1) process for time series of counts. Communications in Statistics - Theory and Methods. 2021; ():1-19.
Chicago/Turabian StyleManuel G. Scotto; Sónia Gouveia. 2021. "On the extremes of the max-INAR(1) process for time series of counts." Communications in Statistics - Theory and Methods , no. : 1-19.
In the atmosphere, aerosols play an important role in climate change, the Earth’s environment and human health. The purpose of this study is to investigate the direct and semi-direct aerosol effects on weather forecasting, focusing on the Iberian Peninsula (IP). To that end, two Weather Research and Forecasting (WRF)-Chem simulations (with and without aerosol feedback) for an entire year (2015) were performed. The model setup includes two nested domains run in two-way mode, allowing the downscaling for the IP domain at a 5 × 5 km2 high-horizontal resolution. The results were explored through agreement of pairs of time series and their spatial variability in order to analyse the importance of including the online-coupled aerosol radiative effect on the meteorological variables: shortwave (solar) radiation, air temperature and precipitation. Significant variations of agreement were found when capturing both temporal and spatial patterns of the analysed meteorological variables. While the spatial distribution of temperature and precipitation is similar throughout the IP domain, with agreement values ranging from 0.87 up to 1.00, the solar radiation presents a distinct spatial pattern with lower agreement values (0.68–0.75) over ocean and higher agreement (0.75–0.98) over land regions. With regard to the spatial differences between simulations, the aerosol contributed to a considerable decrease in annual mean and maximum radiation (up to 20 and 40 Wm−2, respectively), slightly impacting the temperature variation (up to 0.5 °C). These results suggest that the aerosol feedback effects should be accounted when performing weather forecasts, and not only for purposes of air quality assessment.
Carlos Silveira; Ana Martins; Sónia Gouveia; Manuel Scotto; Ana Miranda; Alexandra Monteiro. The Role of the Atmospheric Aerosol in Weather Forecasts for the Iberian Peninsula: Investigating the Direct Effects Using the WRF-Chem Model. Atmosphere 2021, 12, 288 .
AMA StyleCarlos Silveira, Ana Martins, Sónia Gouveia, Manuel Scotto, Ana Miranda, Alexandra Monteiro. The Role of the Atmospheric Aerosol in Weather Forecasts for the Iberian Peninsula: Investigating the Direct Effects Using the WRF-Chem Model. Atmosphere. 2021; 12 (2):288.
Chicago/Turabian StyleCarlos Silveira; Ana Martins; Sónia Gouveia; Manuel Scotto; Ana Miranda; Alexandra Monteiro. 2021. "The Role of the Atmospheric Aerosol in Weather Forecasts for the Iberian Peninsula: Investigating the Direct Effects Using the WRF-Chem Model." Atmosphere 12, no. 2: 288.
The study of coastal boulder accumulations generated by extreme marine events, and of the energy and frequency involved in boulder transport, is of paramount importance in understanding the risk associated with extreme marine inundations. One of the frequently asked questions is whether the deposits are storm or tsunami-related, both events being characterized by different return periods. Boulder transport by storms was monitored on the west coast of Portugal. Significant changes were detected in boulders' position as a result of extreme inundation by the 2013/2014 winter storms. Results presented in this work indicate that the wave power associated with the “Christina” and “Nadja” storms occur once every three years. However, this interval is not supported by field observations of boulder displacement, which suggests that wave power over-predicts boulder movement in the study area. Furthermore, wave parameters from the “Christina” and “Nadja” storms were very similar, but have generated different impacts in the boulder accumulation described herein. Differences include the magnitude and direction of boulder movement, and are most likely associated with distinct tidal levels during the events. Higher tide levels generated an increase in the sea surface level and thus in the reach of waves, which generated displacement of larger boulders and consequent cross-shore contribution in boulder transport. Regardless, the combination of monitoring campaigns, wave data, and statistical modelling of extreme values indicate that boulder transport by storms is more frequent than initially expected. Based on recorded boulder movements, we present a conceptual model for boulder ridge formation and development and identify significant control of incoming flow by local geomorphological/topographical features. Storm events, not less frequent tsunamis, are identified as the events responsible for modulating this rocky coastline. These results question a direct attribution of coastal boulder deposits to tsunamis in coastal regions with a high risk of tsunami inundation.
Maria Alexandra Oliveira; Manuel Scotto; Susana Barbosa; César Freire de Andrade; Maria Da Conceição Freitas. Morphological controls and statistical modelling of boulder transport by extreme storms. Marine Geology 2020, 426, 106216 .
AMA StyleMaria Alexandra Oliveira, Manuel Scotto, Susana Barbosa, César Freire de Andrade, Maria Da Conceição Freitas. Morphological controls and statistical modelling of boulder transport by extreme storms. Marine Geology. 2020; 426 ():106216.
Chicago/Turabian StyleMaria Alexandra Oliveira; Manuel Scotto; Susana Barbosa; César Freire de Andrade; Maria Da Conceição Freitas. 2020. "Morphological controls and statistical modelling of boulder transport by extreme storms." Marine Geology 426, no. : 106216.
One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges, it can be described by scaling relationships in the form of power laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate prediction.
Christian L. E. Franzke; Susana Barbosa; Richard Blender; Hege‐Beate Fredriksen; Thomas Laepple; Fabrice Lambert; Tine Nilsen; Kristoffer Rypdal; Martin Rypdal; Scotto Manuel G; Stéphane Vannitsem; Nicholas W. Watkins; Lichao Yang; Naiming Yuan. The Structure of Climate Variability Across Scales. Reviews of Geophysics 2020, 58, 1 .
AMA StyleChristian L. E. Franzke, Susana Barbosa, Richard Blender, Hege‐Beate Fredriksen, Thomas Laepple, Fabrice Lambert, Tine Nilsen, Kristoffer Rypdal, Martin Rypdal, Scotto Manuel G, Stéphane Vannitsem, Nicholas W. Watkins, Lichao Yang, Naiming Yuan. The Structure of Climate Variability Across Scales. Reviews of Geophysics. 2020; 58 (2):1.
Chicago/Turabian StyleChristian L. E. Franzke; Susana Barbosa; Richard Blender; Hege‐Beate Fredriksen; Thomas Laepple; Fabrice Lambert; Tine Nilsen; Kristoffer Rypdal; Martin Rypdal; Scotto Manuel G; Stéphane Vannitsem; Nicholas W. Watkins; Lichao Yang; Naiming Yuan. 2020. "The Structure of Climate Variability Across Scales." Reviews of Geophysics 58, no. 2: 1.
Viral load values and CD4\(^{+}\)T cells count are markers currently evaluated in the clinical follow-up of HIV/AIDS patients. In this context, it is relevant to develop methods that provide a more complete temporal description of these markers, e.g. in between clinical appointments. To this end, we combine a mathematical model and a Bayesian methodology to estimate trajectories from a set of observed values. Also, we construct a variation band containing the most central trajectories for one patient, by exploring the range of values in the a posteriori distributions. The methods are illustrated with simulated data.
Diana Rocha; Manuel Scotto; Carla Pinto; João Tavares; Sónia Gouveia. Simulation Study of HIV Temporal Patterns Using Bayesian Methodology. Springer Texts in Business and Economics 2019, 145 -154.
AMA StyleDiana Rocha, Manuel Scotto, Carla Pinto, João Tavares, Sónia Gouveia. Simulation Study of HIV Temporal Patterns Using Bayesian Methodology. Springer Texts in Business and Economics. 2019; ():145-154.
Chicago/Turabian StyleDiana Rocha; Manuel Scotto; Carla Pinto; João Tavares; Sónia Gouveia. 2019. "Simulation Study of HIV Temporal Patterns Using Bayesian Methodology." Springer Texts in Business and Economics , no. : 145-154.
In this paper a multivariate integer-valued autoregressive model of order one with periodic time-varying parameters, and driven by a periodic innovations sequence of independent random vectors is introduced and studied in detail. Emphasis is placed on models with periodic multivariate negative binomial innovations. Basic probabilistic and statistical properties of the novel model are discussed. Aiming to reduce computational burden arising from the use of the conditional maximum likelihood method, a composite likelihood-based approach is adopted. The performance of such method is compared with that of some traditional competitors, namely moment estimators and conditional maximum likelihood estimators. Forecasting is also addressed. Furthermore, an application to a real data set concerning the monthly number of fires in three counties in Portugal is presented.
Cláudia Santos; Isabel Pereira; Manuel G. Scotto. On the theory of periodic multivariate INAR processes. Statistical Papers 2019, 62, 1291 -1348.
AMA StyleCláudia Santos, Isabel Pereira, Manuel G. Scotto. On the theory of periodic multivariate INAR processes. Statistical Papers. 2019; 62 (3):1291-1348.
Chicago/Turabian StyleCláudia Santos; Isabel Pereira; Manuel G. Scotto. 2019. "On the theory of periodic multivariate INAR processes." Statistical Papers 62, no. 3: 1291-1348.
In this paper, an integer-valued autoregressive model of order one (INAR(1)) with time-varying parameters and driven by a periodic sequence of innovations is introduced. The proposed INAR(1) model is based on the signed thinning operator defined by Kachour and Truquet (2011) and conveniently adapted to the periodic case. Basic notations and definitions concerning the periodic signed thinning operator are provided. Based on this thinning operator, Chesneau and Kachour (2012) established a signed INAR(1) model. Motivated by the work of Chesneau and Kachour (2012), we introduce a periodic model, denoted by S-PINAR(1), with period s. In contrast to conventional INAR(1) models, these models are defined in \(\mathbb {Z}\) allowing for negative values both for the series and its autocorrelation function. For a proper \(\mathbb {Z}\)-valued time series, a distribution for the innovation term defined on \(\mathbb {Z}\) is required. The S-PINAR(1) model assumes a specific innovation distribution, the Skellam distribution. Regarding parameter estimation, two methods are considered: conditional least squares and conditional maximum likelihood. The performance of the S-PINAR(1) model is assessed through a simulation study.
Cláudia Santos; Isabel Pereira; Manuel Scotto. Periodic INAR(1) Models with Skellam-Distributed Innovations. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 64 -78.
AMA StyleCláudia Santos, Isabel Pereira, Manuel Scotto. Periodic INAR(1) Models with Skellam-Distributed Innovations. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():64-78.
Chicago/Turabian StyleCláudia Santos; Isabel Pereira; Manuel Scotto. 2019. "Periodic INAR(1) Models with Skellam-Distributed Innovations." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 64-78.
The aim of this work is to assess the modeling performance of two bivariate models for time series of counts, within the context of a forest fires analysis in two counties of Portugal. The first model is a periodic bivariate integer-valued autoregressive (PBINAR), easily interpreted due to the PINAR description of each component. The alternative model is a bivariate dynamic factor (BDF) that has a flexible structure, with the dynamics described through the mean value of each component that is a function of latent factors. The results reveal that BDF model exhibits a better ability to capture the dependence structure.
Magda Monteiro; Isabel Pereira; Manuel G. Scotto. Bivariate models for time series of counts: A comparison study between PBINAR models and dynamic factor models. Communications in Statistics - Simulation and Computation 2019, 50, 1873 -1887.
AMA StyleMagda Monteiro, Isabel Pereira, Manuel G. Scotto. Bivariate models for time series of counts: A comparison study between PBINAR models and dynamic factor models. Communications in Statistics - Simulation and Computation. 2019; 50 (7):1873-1887.
Chicago/Turabian StyleMagda Monteiro; Isabel Pereira; Manuel G. Scotto. 2019. "Bivariate models for time series of counts: A comparison study between PBINAR models and dynamic factor models." Communications in Statistics - Simulation and Computation 50, no. 7: 1873-1887.
EURO-CORDEX is an international initiative which provides regional climate projections based on multiple dynamical and empirical–statistical downscaling models. The goal of this work is to analyse the agreement between projections of the CLMCOM-CCLM4-8-17 (CLMCOM) and SMHI-RCA4 (SMHI) models across Europe. The variables temperature, precipitation and solar radiation were considered for a historical period (1971–2005) and for a future scenario (2006–2040). The overall agreement (\(\mathcal {A}\)) is defined as the normalised area of the magnitude-squared coherence function over the frequency range (averaged over time), being 0 for no agreement and 1 for total agreement between models. The relative mean difference (\(\mathcal {M}\)) and difference between the coefficients of variation (\(\mathcal {V}\)) are also explored, since coherence analysis cannot evaluate differences in mean and variability. Agreement values ranging from 0.32 to 0.74, 0.28 to 0.69 and 0.32 to 0.58 were found for temperature, precipitation and solar radiation, respectively, for the historical period. In all cases, the results show better agreement for lower than higher frequencies. Overall, the time series from both models behave fairly similarly for lower frequencies (i.e. the trend of the time series), while for higher frequencies (i.e. rapid changes in the time series), the similarities between the models are less consistent. For temperature, the \(\mathcal {M}\) and \(\mathcal {V}\) values are smaller than 2.5%, while for precipitation and solar radiation they can exceed 50% and 35%, respectively. Further analysis revealed that the contribution of winter and summer differs considerably for \(\mathcal {M}\) and \(\mathcal {V}\) values. In conclusion, it seems that such models can provide fairly similar results when considering long periods of time.
Ana Martins; Sandra Rafael; Alexandra Monteiro; Manuel Scotto; Sónia Gouveia. Euro-Cordex Regional Projection Models: What Kind of Agreement for Europe? Mathematical Geosciences 2019, 51, 1021 -1035.
AMA StyleAna Martins, Sandra Rafael, Alexandra Monteiro, Manuel Scotto, Sónia Gouveia. Euro-Cordex Regional Projection Models: What Kind of Agreement for Europe? Mathematical Geosciences. 2019; 51 (8):1021-1035.
Chicago/Turabian StyleAna Martins; Sandra Rafael; Alexandra Monteiro; Manuel Scotto; Sónia Gouveia. 2019. "Euro-Cordex Regional Projection Models: What Kind of Agreement for Europe?" Mathematical Geosciences 51, no. 8: 1021-1035.
The analysis of low integer-valued time series is an area of growing interest as time series of counts arising from many different areas have become available in the last three decades. Statistical quality control, computer science, economics and finance, medicine and epidemiology and environmental sciences are just some of the fields that we can mention to point out the wide variety of contexts from which discrete time series have emerged. In many of these areas it is not just the statistical modelling of count data that matters. For instance, in environmental sciences or epidemiology, surveillance and risk analysis are critical and timely intervention is mandatory in order to ensure safety and public health. Actually, a major issue in the analysis of a large variety of random phenomena relates to the ability to detect and warn the occurrence of a catastrophe or some other event connected with an alarm system. In this work, the principles for the construction of optimal alarm systems are discussed and their implementation is described. As there is no unifying approach to the modelling of all integer-valued time series, we will focus our attention in the class of observation-driven models. The implementation of the optimal alarm system will be described in detail for a particular non-linear model in this class, the INteger-valued Asymmetric Power ARCH, or, in short, INAPARCH(p, q).
Maria Da Conceição Costa; Isabel Pereira; Manuel G. Scotto. Surveillance in Discrete Time Series. mODa 11 - Advances in Model-Oriented Design and Analysis 2018, 197 -212.
AMA StyleMaria Da Conceição Costa, Isabel Pereira, Manuel G. Scotto. Surveillance in Discrete Time Series. mODa 11 - Advances in Model-Oriented Design and Analysis. 2018; ():197-212.
Chicago/Turabian StyleMaria Da Conceição Costa; Isabel Pereira; Manuel G. Scotto. 2018. "Surveillance in Discrete Time Series." mODa 11 - Advances in Model-Oriented Design and Analysis , no. : 197-212.
Motivated by a large dataset containing time series of weekly number of rainy days collected over two thousand locations across Europe and Russia for the period 2000–2010, we propose a new class of ARMA-like model for time series of bounded counts, which can also handle extra-binomial variation. We abbreviate this model as bvARMA, as it is based upon a novel operation referred to as binomial variation. After having discussed important stochastic properties and proposed a model-fitting approach relying on maximum likelihood estimation, we apply the bvARMA model family to the rainy-days time series. Results show that both bvAR and bvMA models are adequate and exhibit a similar performance. Furthermore, bvARMA results outperform those obtained by fitting ordinary discrete ARMA (NDARMA) models of the same order.
Sónia Gouveia; Tobias A. Möller; Christian H. Weiß; Manuel G. Scotto. A full ARMA model for counts with bounded support and its application to rainy-days time series. Stochastic Environmental Research and Risk Assessment 2018, 32, 2495 -2514.
AMA StyleSónia Gouveia, Tobias A. Möller, Christian H. Weiß, Manuel G. Scotto. A full ARMA model for counts with bounded support and its application to rainy-days time series. Stochastic Environmental Research and Risk Assessment. 2018; 32 (9):2495-2514.
Chicago/Turabian StyleSónia Gouveia; Tobias A. Möller; Christian H. Weiß; Manuel G. Scotto. 2018. "A full ARMA model for counts with bounded support and its application to rainy-days time series." Stochastic Environmental Research and Risk Assessment 32, no. 9: 2495-2514.
In this note, we introduce a discrete counterpart of the conventional max-autoregressive moving-average process of Davis and Resnick (1989), based on the binomial thinning operator and driven by a sequence of i. i. d. nonnegative integer-valued random variables with a finite range of counts. Basic probabilistic and statistical properties of this new class of models are discussed in detail, namely the existence of a stationary distribution, and how observations’ and innovations’ distributions are related to each other. Furthermore, parameter estimation is also addressed.
Christian H. Weiß; Manuel Scotto; Tobias A. Möller; Sónia Gouveia. The max-BARMA models for counts with bounded support. Statistics & Probability Letters 2018, 143, 28 -36.
AMA StyleChristian H. Weiß, Manuel Scotto, Tobias A. Möller, Sónia Gouveia. The max-BARMA models for counts with bounded support. Statistics & Probability Letters. 2018; 143 ():28-36.
Chicago/Turabian StyleChristian H. Weiß; Manuel Scotto; Tobias A. Möller; Sónia Gouveia. 2018. "The max-BARMA models for counts with bounded support." Statistics & Probability Letters 143, no. : 28-36.
This paper proposes a discrete counterpart of the conventional max-autoregressive process of order one. It is based on the so-called binomial thinning operator and driven by a sequence of independent and identically distributed nonnegative integer-valued random variables with either regularly varying right tail or exponential-type right tail. Basic probabilistic and statistical properties of the process are discussed in detail, including the analysis of conditional moments, transition probabilities, the existence and uniqueness of a stationary distribution, and the relationship between the observations’ and innovations’ distribution. We also provide conditions on the marginal distribution of the process to ensure that the innovations’ distribution exists and is well defined. Several examples of families of distributions satisfying such conditions are presented, but also some counterexamples are analyzed. Furthermore, the analysis of its extremal behavior is also considered. In particular, we look at the limiting distribution of sample maxima and its corresponding extremal index.
Manuel G. Scotto; Christian H. Weiß; Tobias A. Möller; Sónia Gouveia. The max-INAR(1) model for count processes. TEST 2017, 27, 850 -870.
AMA StyleManuel G. Scotto, Christian H. Weiß, Tobias A. Möller, Sónia Gouveia. The max-INAR(1) model for count processes. TEST. 2017; 27 (4):850-870.
Chicago/Turabian StyleManuel G. Scotto; Christian H. Weiß; Tobias A. Möller; Sónia Gouveia. 2017. "The max-INAR(1) model for count processes." TEST 27, no. 4: 850-870.
Symbolic or categorical sequences occur in many contexts and can be characterized, for example, by integer-valued intersymbol distances or binary-valued indicator sequences. The analysis of these numerical sequences often sheds light on the properties of the original symbolic sequences. This work introduces new statistical tools for exploring auto-correlation structure in the indicator sequences, for the specific case of deoxyribonucleic acid (DNA) sequences. It is known that the probability distribution of internucleotide distances of DNA sequences deviates significantly from the distribution obtained by assuming independent random placement (i.e. the geometric distribution) and that the deviations can be used either to discriminate between species or to build phylogenetic trees. To investigate the extent to which auto-correlation structure explains these deviations, the 0–1 indicator sequence of each nucleotide (A, C, G and T) is endowed with a binary auto-regressive (AR) model of optimum order. The corresponding binary AR geometric distribution is derived analytically and compared with the observed internucleotide distance distribution by appropriate goodness-of-fit testing. Results in 34 mitochondrial DNA sequences show that the hypothesis of equal observed/expected frequencies is seldom rejected when a binary AR model is considered instead of independence (76/136 versus 125/136 rejections at the 1% level), in spite of ?2-testing tending to reject for large samples, regardless of how close observed/expected values are. Furthermore, binary AR structure also leads to a median discrepancy reduction of 90% for G, 80% for C, 60% for T and 30% for nucleotide A. Therefore, these models are useful to describe the dependences within a given nucleotide and encourage the development of a model-based framework to compact internucleotide distance information and to understand DNA differences among species further.
Sónia Gouveia; Manuel G. Scotto; Christian H. Weiß; Paulo Jorge S. G. Ferreira. Binary auto-regressive geometric modelling in a DNA context. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2016, 66, 253 -271.
AMA StyleSónia Gouveia, Manuel G. Scotto, Christian H. Weiß, Paulo Jorge S. G. Ferreira. Binary auto-regressive geometric modelling in a DNA context. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2016; 66 (2):253-271.
Chicago/Turabian StyleSónia Gouveia; Manuel G. Scotto; Christian H. Weiß; Paulo Jorge S. G. Ferreira. 2016. "Binary auto-regressive geometric modelling in a DNA context." Journal of the Royal Statistical Society: Series C (Applied Statistics) 66, no. 2: 253-271.
The Asymmetric Power ARCH representation for the volatility was introduced by Ding et al. (J Empir Financ 1:83–106, 1993) in order to account for asymmetric responses in the volatility in the analysis of continuous-valued financial time series like, for instance, the log-return series of foreign exchange rates, stock indices, or share prices. As reported by Brännäs and Quoreshi (Appl Financ Econ 20:1429–1440, 2010), asymmetric responses in volatility are also observed in time series of counts such as the number of intra-day transactions in stocks. In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric overdispersion. Basic probabilistic and statistical properties are summarized and parameter estimation is discussed. A simulation study is presented to illustrate the proposed model. Finally, an empirical application to a set of data concerning the daily number of stock transactions is also presented to attest for its practical applicability in data analysis.
Maria Da Conceição Costa; Manuel Scotto; Isabel Pereira. Integer-Valued APARCH Processes. mODa 11 - Advances in Model-Oriented Design and Analysis 2016, 189 -202.
AMA StyleMaria Da Conceição Costa, Manuel Scotto, Isabel Pereira. Integer-Valued APARCH Processes. mODa 11 - Advances in Model-Oriented Design and Analysis. 2016; ():189-202.
Chicago/Turabian StyleMaria Da Conceição Costa; Manuel Scotto; Isabel Pereira. 2016. "Integer-Valued APARCH Processes." mODa 11 - Advances in Model-Oriented Design and Analysis , no. : 189-202.
Ana M. J. Cruz; Célia Alves; Sónia Gouveia; Manuel G. Scotto; Maria Do Carmo Freitas; Hubert Th Wolterbeek. A wavelet-based approach applied to suspended particulate matter time series in Portugal. Air Quality, Atmosphere & Health 2016, 9, 847 -859.
AMA StyleAna M. J. Cruz, Célia Alves, Sónia Gouveia, Manuel G. Scotto, Maria Do Carmo Freitas, Hubert Th Wolterbeek. A wavelet-based approach applied to suspended particulate matter time series in Portugal. Air Quality, Atmosphere & Health. 2016; 9 (8):847-859.
Chicago/Turabian StyleAna M. J. Cruz; Célia Alves; Sónia Gouveia; Manuel G. Scotto; Maria Do Carmo Freitas; Hubert Th Wolterbeek. 2016. "A wavelet-based approach applied to suspended particulate matter time series in Portugal." Air Quality, Atmosphere & Health 9, no. 8: 847-859.
We introduce a new class of integer-valued self-exciting threshold models, which is based on the binomial autoregressive model of order one as introduced by McKenzie (Water Resour Bull 21:645–650, 1985. doi:10.1111/j.1752-1688.1985.tb05379.x). Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. Finally, the performance of these models is illustrated through a simulation study and an empirical application to a set of measle cases in Germany.
Tobias A. Möller; Maria Eduarda Silva; Christian H. Weiß; Manuel G. Scotto; Isabel Pereira. Self-exciting threshold binomial autoregressive processes. AStA Advances in Statistical Analysis 2015, 100, 369 -400.
AMA StyleTobias A. Möller, Maria Eduarda Silva, Christian H. Weiß, Manuel G. Scotto, Isabel Pereira. Self-exciting threshold binomial autoregressive processes. AStA Advances in Statistical Analysis. 2015; 100 (4):369-400.
Chicago/Turabian StyleTobias A. Möller; Maria Eduarda Silva; Christian H. Weiß; Manuel G. Scotto; Isabel Pereira. 2015. "Self-exciting threshold binomial autoregressive processes." AStA Advances in Statistical Analysis 100, no. 4: 369-400.
The classification of multivariate time series in terms of their corresponding temporal dependence patterns is a common problem in geosciences, particularly for large datasets resulting from environmental monitoring networks. Here a wavelet-based clustering approach is applied to sea level and atmospheric pressure time series at tide gauge locations in the Baltic Sea. The resulting dendrogram discriminates three spatially-coherent groups of stations separating the southernmost tide gauges, reflecting mainly high-frequency variability driven by zonal wind, from the middle-basin stations and the northernmost stations dominated by lower-frequency variability and the response to atmospheric pressure.
S. M. Barbosa; Sónia Gouveia; Manuel Scotto; Andres Modesto Alonso. Wavelet-Based Clustering of Sea Level Records. Mathematical Geosciences 2015, 48, 149 -162.
AMA StyleS. M. Barbosa, Sónia Gouveia, Manuel Scotto, Andres Modesto Alonso. Wavelet-Based Clustering of Sea Level Records. Mathematical Geosciences. 2015; 48 (2):149-162.
Chicago/Turabian StyleS. M. Barbosa; Sónia Gouveia; Manuel Scotto; Andres Modesto Alonso. 2015. "Wavelet-Based Clustering of Sea Level Records." Mathematical Geosciences 48, no. 2: 149-162.
During the summer season, ozone concentrations regularly exceed the legislation limits in the North of Portugal, namely at Douro Norte monitoring station. The origin of such ozone episodes has been widely reported in several studies although uncertainties regarding its origin still remain. This work intends to investigate how the ozone concentrations measured at the Douro Norte nearest stations, located at west and east directions, are related to those measured at Douro Norte by means of coherence and phase transformations methods. The episodes were selected according to the magnitude of the hourly ozone peaks and the occurrence of exceedances of the threshold value at least in two sites. The results point out that 60 % of the selected episodes highlight significant dependence between Douro Norte station and the other two monitoring sites, with different phase signal and a delay range from 2 to 4 h.
Alexandra Monteiro; S. Gouveia; M. G. Scotto; J. Lopes; Carla Gama; M. Feliciano; A. I. Miranda. Investigating ozone episodes in Portugal: a wavelet-based approach. Air Quality, Atmosphere & Health 2015, 9, 775 -783.
AMA StyleAlexandra Monteiro, S. Gouveia, M. G. Scotto, J. Lopes, Carla Gama, M. Feliciano, A. I. Miranda. Investigating ozone episodes in Portugal: a wavelet-based approach. Air Quality, Atmosphere & Health. 2015; 9 (7):775-783.
Chicago/Turabian StyleAlexandra Monteiro; S. Gouveia; M. G. Scotto; J. Lopes; Carla Gama; M. Feliciano; A. I. Miranda. 2015. "Investigating ozone episodes in Portugal: a wavelet-based approach." Air Quality, Atmosphere & Health 9, no. 7: 775-783.