ABSTRACT
In this study, we introduce a novel moving-average model for analyzing stationary time-series observed irregularly in time. The process is strictly stationary and ergodic under normality and ...weakly stationary when normality is not assumed. Maximum likelihood (ML) estimation can be efficiently carried out through a Kalman algorithm obtained from the state-space representation of the model. The Kalman algorithm has order O(n) (where n is the number of observations in the sequence), from which it is possible to efficiently generate parameter estimators, linear predictors, and their mean-squared errors. Two procedures were developed for assessing parameter estimation errors: one based on the Hessian of the likelihood function and another one based on the bootstrap method. The behaviour of these estimators was assessed through Monte Carlo experiments. Both methods give accurate estimation performance, even with relatively small number of observations. Moreover, it is shown that for non-Gaussian data, specifically for the Student's t and generalized error distributions, the parameters of the model can be estimated precisely by ML. The proposed model is compared to the continuous autoregressive moving average (MA) models, showing better performance when the MA parameter is negative or close to one. We illustrate the implementation of the proposed model with light curves of variable stars from the OGLE and HIPPARCOS surveys and stochastic objects from Zwicky Transient Facility. The results suggest that the irregular MA model is a suitable alternative for modelling astronomical light curves, particularly when they have negative autocorrelation.
A Class of Antipersistent Processes Bondon, Pascal; Palma, Wilfredo
Journal of time series analysis,
March 2007, Letnik:
28, Številka:
2
Journal Article
Recenzirano
Odprti dostop
. We introduce a class of stationary processes characterized by the behaviour of their infinite moving average parameters. We establish the asymptotic behaviour of the covariance function and the ...behaviour around zero of the spectral density of these processes, showing their antipersistent character. Then, we discuss the existence of an infinite autoregressive representation for this family of processes, and we present some consequences for fractional autoregressive moving average models.
Although 95th percentile-based normal limits are recommended instead of conventional criteria of normality to guide pulmonary function test (PFT) readings, we have found no objective assessment of ...how the choice of normal limits might influence PFT interpretation.
We did a retrospective comparison of PFT readings referenced to conventional criteria of normality versus independent repeat assessments influenced by 95th percentile-based normal limits in 166 veterans. We also conducted a nationwide telephone survey of VA Hospital PFT laboratories.
Discordant readings occurred in only 7.2% of 616 individual PFTs; however, these discrepancies could potentially influence at least one component of the PFT report of 26.5% of our subjects. The 95th percentile-based normal limits were used by only 40% of VA PFT laboratories, without relationship to geography or hospital size.
Discrepancies between 95th percentile-based and conventional normal limits can potentially influence PFT readings, and 95th percentile-based criteria are not used in the majority of VA PFT laboratories.
The variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. ...However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.Original Abstract: Heuristicke metode nizanja vrijednosti i varijabli su kljucni element u ogranicenom programiranju. Poznate su kao strategija nabrajanja i mogu znacajno utjecati na postupak rjesavanja problema. Medutim, prilicno je tesko izabrati odgovarajuci heuristicki postupak jer je komplicirano predvidjeti njihovo ponasanje. U zadnje je vrijeme za tu svrhu predlozeno samostalno (autonomno) pretrazivanje. Ideja je da se strategije koje su se pokazale losima tijekom postupka rjesavanja dinamicki zamijene onima koje vise obecavaju. Ta se zamjena izvodi koristenjem funkcije izbora, koja u zadanom vremenu procijenjuje ponudenu strategiju preko indikatora kvalitete. Vaznu ulogu u torn procesu ima optimizator kojemu je cilj fino podesavanje funkcije izbora kako bi se garantirala precizna procjena strategija. U ovom radu evaluiramo karakteristike dviju jakih funkcija izbora: prvu podrzava genetski algoritam, a drugu optimizator roja cestica. Dajemo interesantne rezultate i demonstriramo mogucnost koristenja tih metoda optimiziranja za samostalno pretrazivanje u kontekstu ogranicenog programiranja.
A three-dimensional numerical model using the Finite Element Method (FEM) was used to diagnose coastal currents off the El Loa River (21° S) in the northern upwelling region of Chile. This site has ...been recognized as an important spawning zone of the southern anchovy
Engraulis ringens. Diagnostic current fields were obtained for summer and winter during the 1997–1998 El Niño conditions and during a “normal” upwelling year. The results show this site as an efficient retention area in the nearshore, because of a reduced cross-shelf flow, a strong alongshore flow and presence of several anticyclonic eddies. A simulated Lagrangian experiment indicated that retention within the nearshore (<10
km) may last for more than 4 days under a steady-state wind condition. Wind regimes and water density fields during 1997–1998 (El Niño) and 1995–1996 (“normal” upwelling) did not cause differences in the general pattern of coastal circulation. However, the magnitudes of both the alongshore and the cross-shelf flows are substantially reduced during El Niño in the nearshore spawning zone, possibly as a consequence of an anomalous water mass in the coastal area. This altered condition may limit the transport and dispersion of anchovy spawning products.
This paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the ...Kalman filter, the exact likelihood function can be computed recursively in a finite number of steps. Furthermore, an approximation to the likelihood function based on the truncated state space equation is considered. Asymptotic properties of these approximate maximum likelihood estimates are established for a class of long-range dependent models, namely, the fractional autoregressive moving average models. Simulation studies show rapid converging properties of the approximate maximum likelihood approach.
The objective of this work is to propose a statistical methodology to handle regression data exhibiting long memory errors and missing values. This type of data appears very often in many areas, ...including hydrology and environmental sciences, among others. A generalized linear model is proposed to deal with this problem and an estimation strategy is developed that combines both classical and Bayesian approaches. The estimation methodology proposed is illustrated with an application to air pollution data which shows the impact of the long memory in the statistical inference and of the missing values on the computations. From a Bayesian standpoint, genuine priors are considered for the parameters of the model which are justified within the context of the air pollution model derivation.
. This paper studies asymptotic properties of the exact maximum likelihood estimates (MLE) for a general class of Gaussian seasonal long‐range‐dependent processes. This class includes the commonly ...used Gegenbauer and seasonal autoregressive fractionally integrated moving average processes. By means of an approximation of the spectral density, the exact MLE of this class are shown to be consistent, asymptotically normal and efficient. Finite sample performance of these estimates is examined by Monte Carlo simulations and it is shown that the estimates behave very well even for moderate sample sizes. The estimation methodology is illustrated by a real‐life Internet traffic example.
Air quality data at Santiago, Chile (PM
10, PM
2.5 and ozone) from 1989 to 1998 are analyzed with the goal of estimating trends in and impacts of public policies on air quality levels. Those ...policies, in effect since the late 1980s, have been essentially aimed at PM
10 pollution abatement. The analyses show that fall and winter air quality has been improving consistently, specially the PM
2.5 levels. The estimated trends for the monthly averages of PM
10 concentrations range from −1.5 to −3.3% per annum, whereas the trends for monthly averages of PM
2.5 concentrations range from −5 to −7% per annum. The monthly averages of ground ozone daily maxima do not have a significant trend for two of the downtown monitor sites; at the other three monitoring sites (including the one with the highest impacts) there is a clear downward trend between −5 and −3% per annum. The seasonal averages of a declimatized ozone production rate show a downward trend from 1988 through 1995, and no additional improvements have occurred thereafter. These mixed results for ground ozone levels are ascribed to a shift in the magnitude and spatial distribution of emissions in the city, and so there is a need for additional ozone abatement policies and further research on air pollution abatement options.
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