Titlebook: Empirical Likelihood and Quantile Methods for Time Series; Efficiency, Robustne Yan Liu,Fumiya Akashi,Masanobu Taniguchi Book 2018 The Auth

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Professionalisierung in der Altenpflegestribution function for any second-order stationary process. We define quantiles of the spectral distribution function in the frequency domain and consider the quantile method for parameter estimation of stationary time series. The estimation method for quantiles is generally formulated by minimizin

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Pflegequalit?t und Qualit?tssicherungregions for unknown parameters. This method has been developed for the statistical inference for independent and identically distributed random variables. To handle serial correlation, an empirical likelihood method is proposed in the frequency domain for second-order stationary processes. The Whitt

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Introduction,o far. One is the time-domain approach, and the other is the frequency-domain approach. In this book, we place the emphasis on the frequency-domain approach to analyzing stationary time series. Prediction problems, including interpolation and extrapolation problems, are discussed for stationary time

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Parameter Estimation Based on Prediction,ce the joint distribution of the time series does not have analytical expression even if the model is identified. Instead, an alternative method is to estimate the parameters of the time series model by minimizing contrast function. We introduce a new class of contrast functions for parameter estima

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Quantile Method for Time Series,stribution function for any second-order stationary process. We define quantiles of the spectral distribution function in the frequency domain and consider the quantile method for parameter estimation of stationary time series. The estimation method for quantiles is generally formulated by minimizin

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Self-weighted GEL Methods for Infinite Variance Processes, heavy-tailed observation, least absolute deviations (LAD) estimators are known to be less sensitive to outliers than the classical least squares regression. This section generalizes the LAD regression-based inference procedure to the self-weighted version, which is a concept originally introduced b