background: in the past three decades, tehran has experienced warmer summer so we need to determine heat-related mortality to establish appropriate public health activities during hot summers. the aim of the present study was to detect heat waves during the last decades and then determine excess mortality in immediate and lagged times. method: an ecological study based on time-series model was ...

klinkenberg permeability is an important parameter in tight gas reservoirs. there are conventional methods for determining it, but these methods depend on core permeability. cores are few in number, but well logs are usually accessible for all wells and provide continuous information. in this regard, regression methods have been used to achieve reliable relations between log readings and klinke...

The paper introduces a functional time series (lagged) regression model. The impulse response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L2 in applications. A spectral approach to the estimation of these coefficients is proposed and asymptotically justified under a general nonparametric condition on the temporal dependence of the ...

The input-output (IO) model is a powerful economic tool with many extended applications. However, one of the widely criticized drawbacks its rather lengthy time lag in data preparation, making it impossible to apply IO high-resolution time-series analysis. conventional thus unfortunately unsuited for In this study, we present an innovative algorithm that integrates linear regression techniques ...

BACKGROUND Because it is widely played, claims that smoking restrictions will adversely affect bingo games is used as an argument against these policies. We used publicly available data from Massachusetts to assess the impact of 100% smoke-free ordinances on profits from bingo and other gambling sponsored by charitable organisations between 1985 and 2001. METHODS We conducted two analyses: (1...

This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. This is a ver...

The present paper deals with the study of the multiple linear regression model for the estimation and prediction of the time series of radon and thoron progeny concentrations in atmosphere. The general purpose of multiple linear regression model is to find the linear relationship between a dependent (or explained) variable and several independent (or predictor) variables. Radon and thoron proge...