Abstract A significant advancement that occurs during the data cleaning stage is estimating missing data. Studies have shown improper handling leads to inaccurate analysis. Furthermore, most studies indicate occurrence of irrespective correlation between attributes. However, an adaptive search procedure helps determine estimates when correlations attributes are considered in process. Firefly Al...

This article considers the CUSUM-based (cumulative sum) test for a change point in a time series. In the case of testing for a mean shift, the traditional Kolmogorov–Smirnov test statistic involves a consistent long-run variance estimator, which is needed to make the limiting null distribution free of nuisance parameters. The commonly used lag-window type long-run variance estimator requires to...

With the recent developments on opening terahertz (THz) spectrum for experimental purposes by Federal Communications Commission, transceivers operating in range of 0.1THz-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation high-speed and high reliability band communication systems should start with providing extensive kn...

We investigate the nonparametric, composite hypothesis testing problem for arbitrary unknown distributions and in the asymptotic regime with the number of hypotheses grows exponentially large. Such type of asymptotic analysis is important in many practical problems, where the number of variations that can exist within a family of distributions can be countably infinite. We introduce the notion ...

The paper examines the Fractional Fourier Transform (FRFT) based technique as a tool for obtaining probability density function and its derivatives, mainly fitting stochastic model with fundamental probabilistic relationships of infinite divisibility. functions are computed, distributional proprieties reviewed Variance-Gamma (VG) model. VG has been increasingly used an alternative to Classical ...

In this paper, we introduce a transformer into the field of credit scoring based on user online behavioral data and develop an end-to-end feature embedded (FE-Transformer) approach. The FE-Transformer neural network is composed two parts: wide part deep part. uses network. output are concentrated in fusion layer. experimental results show that learning model proposed paper outperforms LR, XGBoo...