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41
The local bootstrap for Markov processes
 J. Statist. Plann. Inference
, 2002
"... A nonparametric bootstrap procedure is proposed for stochastic processes which follow a general autoregressive structure. The procedure generates bootstrap replicates by locally resampling the original set of observations reproducing automatically its dependence properties. It avoids an initial non ..."
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Cited by 14 (2 self)
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A nonparametric bootstrap procedure is proposed for stochastic processes which follow a general autoregressive structure. The procedure generates bootstrap replicates by locally resampling the original set of observations reproducing automatically its dependence properties. It avoids an initial nonparametric estimation of process characteristics in order to generate the pseudotime series and the bootstrap replicates mimic several of the properties of the original process. Applications of the procedure in nonlinear time series analysis are considered and theoretically justi ed; some simulated and real data examples are discussed.
Coupling stochastic models of different time scales, Water Resour
 of 15 W12406 SCHOUPS ET AL.: RELIABLE CONJUNCTIVE USE RULES W12406
, 2001
"... Abstract. A methodology is proposed for coupling stochastic models of hydrologic processes applying to different time scales so that time series generated by the different models be consistent. Given two multivariate time series, generated by two separate (unrelated) stochastic models of the same hy ..."
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Cited by 8 (4 self)
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Abstract. A methodology is proposed for coupling stochastic models of hydrologic processes applying to different time scales so that time series generated by the different models be consistent. Given two multivariate time series, generated by two separate (unrelated) stochastic models of the same hydrologic process, each applying to a different time scale, a transformation is developed (referred to as a coupling transformation) that appropriately modifies the time series of the lowerlevel (finer) time scale so that this series be consistent with the time series of the higherlevel (coarser) time scale without affecting the secondorder stochastic structure of the former and also establishing appropriate correlations between the two time series. The coupling transformation is based on a developed generalized mathematical proposition, which ensures preservation of marginal and joint secondorder statistics and of linear relationships between lower and higherlevel processes. Several specific forms of the coupling transformation are studied, from the simplest single variate to the full multivariate. In addition, techniques for evaluating parameters of the coupling transformation based on second order moments of the lowerlevel process are studied. Furthermore, two methods are proposed to enable preservation of the skewness of the processes, in addition to that of secondorder statistics. The overall methodology can be applied to problems involving disaggregation of annual to seasonal and seasonal to subseasonal time scales, as well as problems involving finer time scales (e.g. daily – hourly), under the only requirement that a specific stochastic model is available for each involved time scale. The performance of the methodology is demonstrated by means of a detailed numerical example. 1
Evaluation of three Simple Imputation Methods for Enhancing Preprocessing of Data with Missing Values
"... One of the important stages of data mining is preprocessing, where the data is prepared for different mining tasks. Often, the realworld data tends to be incomplete, noisy, and inconsistent. It is very common that the data are not obtainable for every observation of every variable. So the presence ..."
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Cited by 4 (1 self)
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One of the important stages of data mining is preprocessing, where the data is prepared for different mining tasks. Often, the realworld data tends to be incomplete, noisy, and inconsistent. It is very common that the data are not obtainable for every observation of every variable. So the presence of missing variables is obvious in the data set. A most important task when preprocessing the data is, to fill in missing values, smooth out noise and correct inconsistencies. This paper presents the missing value problem in data mining and evaluates some of the methods generally used for missing value imputation. In this work, three simple missing value imputation methods are implemented namely (1) Constant substitution, (2) Mean attribute value substitution and (3) Random attribute value substitution. The performance of the three missing value imputation algorithms were measured with respect to different rate or different percentage of missing values in the data set by using some known clustering methods. To evaluate the performance, the standard WDBC data set has been used.
Survey of stochastic models for wind and seastate time series

, 2005
"... The knowledge of sea state and wind conditions is of central importance for many offshore or nearshore operations. In this paper, we make a complete survey of stochastic models for sea state and wind time series. We begin the presentation with methods based on Gaussian processes and non parametric r ..."
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Cited by 4 (3 self)
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The knowledge of sea state and wind conditions is of central importance for many offshore or nearshore operations. In this paper, we make a complete survey of stochastic models for sea state and wind time series. We begin the presentation with methods based on Gaussian processes and non parametric resampling methods for time series are introduced followed by various parametric models. Finally we propose an original statistical method, based on Monte Carlo goodnessoffit tests, for model validation and comparison. The use of this method is illustrated by an example on wind speed data in North Atlantic.
Using largescale climate information to forecast seasonal streamflow in the Truckee and
, 2003
"... The final copy of this thesis has been examined by the signators, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. ..."
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Cited by 4 (1 self)
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The final copy of this thesis has been examined by the signators, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline.
2005: Seasonal forecasting of Thailand summer monsoon rainfall
 Intl. J. Climatology
"... This paper describes the development of a statistical forecasting method for summer monsoon rainfall over Thailand. Predictors of Thailand summer (AugustOctober) monsoon rainfall are identified from the largescale oceanatmospheric circulation variables (i.e., sea surface temperature and sea level ..."
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Cited by 2 (0 self)
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This paper describes the development of a statistical forecasting method for summer monsoon rainfall over Thailand. Predictors of Thailand summer (AugustOctober) monsoon rainfall are identified from the largescale oceanatmospheric circulation variables (i.e., sea surface temperature and sea level pressure) in the IndoPacific region. The identified predictors are part of the broader El Niño Southern Oscillation (ENSO) phenomenon. The predictors exhibit significant relationship to the summer rainfall only during the post1980 period when the Thailand summer rainfall also shows a relationship with ENSO. Two methods for generating ensemble forecasts are adapted. The first is the traditional linear regression, and the second is a local polynomial based nonparametric method. The associated predictive standard errors are used for generating ensembles. Both the methods exhibit significant comparable skills in a crossvalidated mode. However, the nonparametric method shows improved skill during extreme years (i.e. wet and dry years). Furthermore, the models provide useful skill at 1~3 month lead time that can have strong impact on resources planning and management.
On Robustness of ModelBased Bootstrap Schemes in Nonparametric Time Series Analysis
, 1997
"... . Theory in time series analysis is often developed in the context of finitedimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is of a more complex structure, it is us ..."
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Cited by 2 (2 self)
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. Theory in time series analysis is often developed in the context of finitedimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is of a more complex structure, it is usually necessary to capture the whole dependence structure asymptotically for the bootstrap to be valid. However, certain modelbased bootstrap methods remain valid for some interesting quantities arising in nonparametric statistics. We generalize the wellknown "whitening by windowing" principle to joint distributions of nonparametric estimators of the autoregression function. As a consequence, we obtain that modelbased nonparametric bootstrap schemes remain valid for supremumtype functionals as long as they mimic the corresponding finitedimensional joint distributions consistently. As an example, we investigate a finite order Markov chain bootstrap in the context of a general stationary ...
Missing Value Imputation Based on Data Clustering *
"... Abstract. We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clusteringbased Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that ..."
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Abstract. We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clusteringbased Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to the instance A using a kernelbased method. Specifically, we first divide the dataset (including the instances with missing values) into clusters. Next, missing values of an instance A are patched up with the plausible values generated from A’s cluster. Extensive experiments show the effectiveness of the proposed method in missing value imputation task. 1