Count-valued time series data are routinely collected in many application areas. We particularly motivated to study the count of daily new cases, arising from COVID-19 spread. First, we propose a Bayesian framework time-varying semiparametric AR(p) model for and then extend it more sophisticated INGARCH model. calculate posterior contraction rates proposed methods with respect average Hellinger...

modeling and analysis of future prices has been hot topic for economic analysts in recent years. traditionally, the complex movements in the prices are usually taken as random or stochastic process. however, they may be produced by a deterministic nonlinear process. accuracy and efficiency of economic models in the short period forecasting is strategic and crucial for business world. nonlinear ...

background : gastric cancer is the one of the most prevalent reason of cancer-related death in the world. survival of patients after surgery involves identifying risk factors. there are various models to detect the effect of risk factors on patients’ survival. the present study aims at evaluating these models. methods : data from 330 gastric cancer patients diagnosed at the iran cancer institut...

Using quarterly Taiwan economic data, we demonstrate that deeper understanding of relations between variables and substantial gains in forecasting can be obtained by applying econometric and statistical tools to the traditional macroeconometric models. The improvement in forecasting accuracy is illustrated by outof-sample forecasts, and the models employed in the comparison include univariate t...

in this paper an efficient three- stage algorithm is developed for software production cost and time estimation. first stage includes a hybrid model composed of cocomo and function points methods to increase estimation accuracy. second stage encompasses paired comparisons matrix of analytical hierarchy process to determine amount of any resources consumed in each step of software production by ...

understanding the genetic regulatory networks, the discovery of interactions between genes, and understanding regulatory processes in a cell at the gene level, is one of the major goals of system biology and computational biology. modeling gene regulatory networks, describing the actions of the cells at the molecular level and is used in medicine and molecular biology applications such as metab...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

This paper proposes an extension of the framework of linear time invariant system identification to linear slowly time-varying system identification. The class of systems considered is described by ordinary differential equations with coefficients varying piecewise polynomially with time. The estimation is performed in the frequency domain using multisine excitation signals within an errors-in-...