Browsing by Author "Palma, W"

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    A ground-level ozone forecasting model for Santiago, Chile
    (WILEY-BLACKWELL, 2002) Jorquera, H; Palma, W; Tapia, J
    A physically based model for ground-level ozone forecasting is evaluated for Santiago, Chile. The model predicts the daily peak ozone concentration, with the daily rise of air temperature as input variable; weekends and rainy days appear as interventions. This model was used to analyse historical data, using the Linear Transfer Function/Finite Impulse Response (LTF/FIR) formalism; the Simultaneous Transfer Function (STF) method was used to analyse several monitoring stations together. Model evaluation showed a good forecasting performance across stations-for low and high ozone impacts-with power of detection (POD) values between 70 and 100%, Heidke's Skill Scores between 40% and 70% and low false alarm rates (FAR). The model consistently outperforms a pure persistence forecast. Model performance was not sensitive to different implementation options. The model performance degrades for two- and three-days ahead forecast, but is still acceptable for the purpose of developing an environmental warning system at Santiago. Copyright (C) 2002 John Wiley Sons, Ltd.
  • Thumbnail Image
    Item
    An intervention analysis of air quality data at Santiago, Chile
    (PERGAMON-ELSEVIER SCIENCE LTD, 2000) Jorquera, H; Palma, W; Tapia, J
    Air quality data at Santiago, Chile (PM10, PM2.5 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 PM10 pollution abatement. The analyses show that fall and winter air quality has been improving consistently, specially the PM2.5 levels. The estimated trends for the monthly averages of PM10 concentrations range from - 1.5 to - 3.3% per annum, whereas the trends for monthly averages of PM2.5 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. (C) 2000 Elsevier Science Ltd. All rights reserved.
  • Thumbnail Image
    Item
    Analysis of the correlation structure of square time series
    (WILEY, 2004) Palma, W; Zevallos, M
    This paper analyses the asymptotic behaviour of the autocorrelation structure exhibited by squares of time series with a Wold expansion where the input error is a sequence of random variables with mean zero and finite kurtosis. Two important cases are discussed: (i) when the errors are independent and, (ii) when the errors are uncorrelated but their squares are correlated. Both situations are addressed when the process exhibits short or long memory. Consequences of these results on certain models widely used in many disciplines are also discussed.
  • Thumbnail Image
    Item
    Data analysis using regression models with missing observations and long-memory: an application study
    (ELSEVIER, 2006) Iglesias, P; Jorquera, H; Palma, W
    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. (c) 2005 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Item
    Estimation and forecasting of long-memory processes with missing values
    (JOHN WILEY & SONS LTD, 1997) Palma, W; Chan, NH
    This paper addresses the issues of maximum likelihood estimation and forecasting of a long-memory time series with missing values. A state-space representation of the underlying long-memory process is proposed. By incorporating this representation with the Kalman filter, the proposed method allows not only fbr an efficient estimation of an ARFIMA model but also for the estimation of future values under the presence of missing data. This procedure is illustrated through an analysis of a foreign exchange data set. An investment scheme is developed which demonstrates the usefulness of the proposed approach. (C) 1997 John Wiley & Sons, Ltd.
  • Thumbnail Image
    Item
    Estimation of seasonal fractionally integrated processes
    (ELSEVIER SCIENCE BV, 2006) Reisen, VA; Rodrigues, AL; Palma, W
    This paper discusses the estimation of fractionally integrated processes with seasonal components. In order to estimate the fractional parameters, we propose several estimators obtained from the regression of the log-periodogram on different bandwidths selected around and/or between the seasonal frequencies. For comparison purposes, the semi-paramenic method introduced in Geweke and Porter-Hudak (1983) and Porter-Hudak (1990) and the maximum-likelihood estimates (ML) are also considered. As indicated by the Monte Carlo simulations, the performance of the estimators proposed is good even for small sample sizes. (c) 2004 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Item
    On the eigenstructure of generalized fractional processes
    (ELSEVIER SCIENCE BV, 2003) Palma, W; Bondon, P
    This work establishes bounds for the eigenvalues of the covariance matrix from a general class of stationary processes. These results are applied to the statistical analysis of the large sample behavior of estimates and testing procedures of generalized long memory models, including Seasonal ARFIMA and k-factor GARMA processes, among others. (C) 2003 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Item
    State space modeling of long-memory processes
    (INST MATHEMATICAL STATISTICS, 1998) Chan, NH; Palma, W
    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.