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Companies employ social media influencers SMIs due to the compelling evidence of their advertising effectiveness, however, more research is required to identify and compare factors driving their success.We investigate the effect of source influence SI on the intention to purchase I2P through the sequential mediation of parasocial relationships PSR with benign envy BE and PSR with the perceived brand influencer fit BIF.Two independent samples N=411; N=355 from Europe and Southeast Asia are used to perform: i PLSSEM analysis to obtain the model's predictive power and ii classification based machine learning ML to evaluate the model's accuracy. Moreover, within study and between studies comparative analyses are performed. We use regression analysis and split test ML technique for validation. Both samples indicate a higher role of trustworthiness and expertise in forming SI. Furthermore, comparative mediation analysis and predictive accuracy scores show that the audience related feature, BE, played a more vital role in affecting/predicting the followers' I2P than the brand-related feature, BIF. Our findings contribute to the knowledge of SMIs' credibility and comparative analysis paradigms and provide a better understanding for marketing practitioners and researchers.

Study of instantaneous dependence among several variable is important in many of the high-dimensional sciences. Multivariate GARCH models are as a standard approach for modelling time-varying covariance matrix such phenomena. Cholesky GARCH is one of these approaches where the time-varying covariance matrix can be written parsimoniously, containing variance components through a diagonal matrix and dependency components through a unit lower triangular matrix with regression coefficients as entries. In this paper, we proposed a stochastic structure for dependency components in Cholesky GARCH model by considering linear regression model as a state-space model and using kalman filtering for estimating regression coefficients. We find that the MSE of stochastic Cholesky GARCH model is smaller than the MSE of other models also show that the stochastic Cholesky GARCH has better performance in compare to another models based on MAE and MSE criterions for the real data., Comment: 16 pages

The HGARCH model allows long-memory impact in volatilities. A new HGARCH model with time-varying amplitude is considered in this paper. We show the stability of the model as well. A score test is introduced to check the time-varying behavior in amplitude. Some value-at-risk tests are applied to evaluate the forecastings. Simulations are provided which provide further support to the proposed model. We have also have shown the competative performance of our model in forecasting, by compairing it with HGARH and FIGARCH models for some period of SP500 indices., Comment: 15 pages. arXiv admin note: text overlap with arXiv:1803.00739

HYGARCH model is basically used to model long-range dependence in volatility. We propose Markov switch smooth-transition HYGARCH model, where the volatility in each state is a time-dependent convex combination of GARCH and FIGARCH. This model provides a flexible structure to capture different levels of volatilities and also short and long memory effects. The necessary and sufficient condition for the asymptotic stability is derived. Forecast of conditional variance is studied by using all past information through a parsimonious way. Bayesian estimations based on Gibbs sampling are provided. A simulation study has been given to evaluate the estimations and model stability. The competitive performance of the proposed model is shown by comparing it with the HYGARCH and smooth-transition HYGARCH models for some period of the \textit{S}\&\textit{P}500 indices based on volatility and value-at-risk forecasts., Comment: 25 pages

Based on independent progressively Type-II censored samples from two-parameter Rayleigh distributions with the same location parameter but different scale parameters, the UMVUE and maximum likelihood estimator of $R=P(Y

Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian $\alpha$-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that estimates parameters of a mixture of sub-Gaussian stable distributions. A comparative study, in the presence of some well-known mixture models, is performed to show the robustness and performance of the mixture of sub-Gaussian $\alpha$-stable distributions for modelling, simulated, synthetic, and real data., Comment: 14 pages 4 figures

HYGARCH process is the commonly used long memory process in modeling the long-rang dependence in volatility. Financial time series are characterized by transition between phases of different volatility levels. The smooth transition HYGARCH (ST-HYGARCH) model is proposed to model time-varying structure with long memory property. The asymptotic behavior of the second moment is studied and an upper bound for it is derived. A score test is developed to check the smooth transition property. The asymptotic behavior of the proposed model and the score test is examined by simulation. The proposed model is applied to the \textit{S}\&\textit{P}500 indices for some period which show evidence of smooth transition property and demonstrates out-performance of the ST-HYGARCH than HYGARCH in forecasting., Comment: 18 pages

We express the joint R\'enyi entropy of progressively censored order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint R\'enyi entropy of progressively Type-II censored data. Then we establish a goodness of fit test statistic based on the R\'enyi Kullback-Leibler information with the progressively Type-II censored data, and compare its performance with the leading test statistic. A Monte Carlo simulation study shows that the proposed test statistic shows better powers than the leading test statistic against the alternatives with monotone increasing, monotone decreasing and nonmonotone hazard functions., Comment: 16 pages

The characteristic feature of semi-selfsimilar process is the invariance of its finite dimensional distributions by certain dilation for specific scaling factor. Estimating the scale parameter $\lambda$ and the Hurst index of such processes is one of the fundamental problem in the literature. We present some iterative method for estimation of the scale and Hurst parameters which is addressed for semi-selfsimilar processes with stationary increments. This method is based on some flexible sampling scheme and evaluating sample variance of increments in each scale intervals $[\lambda^{n-1}, \lambda^n)$, $n\in \mathbb{N}$. For such iterative method we find the initial estimation for the scale parameter by evaluating cumulative sum of moving sample variances and also by evaluating sample variance of preceding and succeeding moving sample variance of increments. We also present a new efficient method for estimation of Hurst parameter of selfsimilar processes. As an example we introduce simple fractional Brownian motion (sfBm) which is semi-selfsimilar with stationary increments. We present some simulations and numerical evaluation to illustrate the results and to estimate the scale for sfBm as a semi-selfsimilar process. We also present another simulation and show the efficiency of our method in estimation of Hurst parameter by comparing its performance with some previous methods., Comment: 15 pages

Strong mixing property holds for a broad class of linear and nonlinear time series models such as ARMA and GARCH models. In this article we study correlation structure of strong mixing sequences, and some asymptotic properties are presented. We also present a new method for detecting change point in correlation structure of strong mixing sequences, and present a nonparametric CUSUM test statistic for this. Asymptotic consistency of this test statistics is shown. This method is applied to simulated data of some linear and nonlinear models and power of the test is evaluated. For linear models, it is shown that this method have a better performance in compare to Berkes et al.(2009)., Comment: 18 pages

A hybrid censoring scheme is a mixture of Type-I and Type-II censoring schemes. We study the estimation of parameters of weighted exponential distribution based on Type-II hybrid censored data. By applying EM algorithm, maximum likelihood estimators are evaluated. Also using Fisher infirmation matrix asymptotic confidence intervals are provided. By applying Markov Chain Monte Carlo techniques Bayes estimators, and corresponding highest posterior density confidence intervals of parameters are obtained. Monte Carlo simulations to compare the performances of the different methods is performed and one data set is analyzed for illustrative purposes., Comment: 15 pages