Nonparametric correlation estimators as the Kendall and Spearman correlation are widely used in the applied sciences. They are often said to be robust, in the sense of being resistant to outlying observations. In this paper we formally study their robustness by means of their influence functions and gross-error sensitivities. Since robustness of an estimator often comes at the price of an incre...

because of the increasing importance of supplying water for the country, water resource management is of paramount importance. predicting precipitation, as one of the most important climatic parameters, is especially important in using water supplies. time series can be used to predict precipitation. time series analysis seems to be a suitable tool for such forecasting. the present work studies...

One of the very important issues discussed in the recent two decades is climate change. Temperature is one of the indicative factors of climate change. The present research aimed at studying temporal variation in temperature over Guwahati, Tezpur, Dibrugarh (Mohanbari) and Silchar stations, Assam, India, during the period 1981–2010. Trends in annual and seasonal temperature series were analyzed...

We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univaria...

background road traffic accidents and their related deaths have become a major concern, particularly in developing countries. iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. in this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying arima models on data...