Asymptotic normality of a robust estimator of the regression function for functional time series data

2010 ◽  
Vol 39 (4) ◽  
pp. 489-500 ◽  
Author(s):  
Mohammed Attouch ◽  
Ali Laksaci ◽  
Elias Ould Saïd
Keyword(s):  
Time Series ◽  
Asymptotic Normality ◽  
Time Series Data ◽  
Regression Function ◽  
Series Data ◽  
Robust Estimator ◽  
Functional Time Series

scholarly journals Asymptotic normality of the relative error regression function estimator for censored and time series data

2021 ◽  
Vol 9 (1) ◽  
pp. 156-178
Author(s):  
Feriel Bouhadjera ◽  
Elias Ould Saïd
Keyword(s):  
Asymptotic Normality ◽  
Relative Error ◽  
Time Series Data ◽  
Asymptotic Variance ◽  
Kernel Estimator ◽  
Regression Function ◽  
Real Data ◽  
Series Data ◽  
Failure Times

Abstract Consider a survival time study, where a sequence of possibly censored failure times is observed with d-dimensional covariate The main goal of this article is to establish the asymptotic normality of the kernel estimator of the relative error regression function when the data exhibit some kind of dependency. The asymptotic variance is explicitly given. Some simulations are drawn to lend further support to our theoretical result and illustrate the good accuracy of the studied method. Furthermore, a real data example is treated to show the good quality of the prediction and that the true data are well inside in the confidence intervals.

scholarly journals FORECASTING OF TEMPERATURE IN BANGLADESH BY USING SLICED FUNCTIONAL TIME SERIES (SFTS) APPROACH

2020 ◽  
Vol 5 (10) ◽  
Author(s):  
FARHANA AKTER BINA
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Prediction Intervals ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Forecast ◽  
Functional Time Series ◽  
Climate Time Series

Climate is a paradigm of a complex system and its changes are global in nature. It is an exciting challenge to predict these changes over the period of different time scales. Time series analysis is one of the most important and major tools to analyze the climate time series data. Temperature is one of the most important climatic parameter. In this research, our main aim is to conduct a study across the country to forecast temperature through a relatively new method of forecasting approach named as sliced functional time series (SFTS). The monthly forecasts were obtained along with prediction intervals. These forecasts were compared with the forecasts obtained from autoregressive integrated moving average (ARIMA) and exponential smoothing state-space (ETS) models based on the accuracy measures and the length of prediction intervals to evaluate the performance of SFTS approach. Keywords: Climate,Functional Time Series,Sliced Functional Time Series, Temperature, Forecast, Forecast Accuracy

scholarly journals Asymptotic normality of the maximum likelihood estimator for cooperative sequential adsorption

2011 ◽  
Vol 43 (3) ◽  
pp. 636-648 ◽  
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Maximum Likelihood Estimator ◽  
Time Series Data ◽  
Series Data ◽  
Adsorption Model ◽  
Likelihood Estimator ◽  
Large Samples ◽  
Sequential Adsorption

We consider statistical inference for a parametric cooperative sequential adsorption model for spatial time series data, based on maximum likelihood. We establish asymptotic normality of the maximum likelihood estimator in the thermodynamic limit. We also perform and discuss some numerical simulations of the model, which illustrate the procedure for creating confidence intervals for large samples.

scholarly journals Robust Regression for Functional Time Series Data

2012 ◽  
Vol 42 (2) ◽  
pp. 125-143 ◽  
Author(s):  
Mohammed Kadi Attouch ◽  
Ali Laksaci ◽  
Elias Ould Sa^|^iuml;d
Keyword(s):  
Time Series ◽  
Robust Regression ◽  
Time Series Data ◽  
Series Data ◽  
Functional Time Series

scholarly journals Asymptotic normality of the maximum likelihood estimator for cooperative sequential adsorption

2011 ◽  
Vol 43 (03) ◽  
pp. 636-648
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Maximum Likelihood Estimator ◽  
Time Series Data ◽  
Series Data ◽  
Adsorption Model ◽  
Likelihood Estimator ◽  
Large Samples ◽  
Sequential Adsorption

We consider statistical inference for a parametric cooperative sequential adsorption model for spatial time series data, based on maximum likelihood. We establish asymptotic normality of the maximum likelihood estimator in the thermodynamic limit. We also perform and discuss some numerical simulations of the model, which illustrate the procedure for creating confidence intervals for large samples.

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