The exponential growth of storage space in blockchain network has become a serious problem to hinder the distribution of blockchain and the expansion of blockchain nodes. In this paper. We propose a security strategy ...
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The exponential growth of storage space in blockchain network has become a serious problem to hinder the distribution of blockchain and the expansion of blockchain nodes. In this paper. We propose a security strategy for distributed storage blockchains, which can delete part of blockchains so that nodes only store part of a blockchain. We design a kind of semi-full node between full node and light node according to the requirement of the strategy, besides describe the process of deleting block and synchronizing block, and the running logic of the semi-full node. Finally, we perform comprehensive experiments of the truncated mcmc random algorithm. The results show that in the case of multi-node, the truncated block will not affect the block chain network. Compared with the traditional block design, our storage strategies can reduce storage requirements under most of situation, thus enable blockchains to be deployed on mobile or smaller storage computers.
Water temperature is a key characteristic defining chemical, physical, and biologic conditions in riverine systems. Models of riverine water quality require many inputs, which are commonly beset by uncertainty. This s...
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Water temperature is a key characteristic defining chemical, physical, and biologic conditions in riverine systems. Models of riverine water quality require many inputs, which are commonly beset by uncertainty. This study presents an uncertainty analysis of inputs to the stream-temperature simulation model HFLUX. This paper's assessment relies on a Markov chain Monte Carlo (mcmc) analysis with the DREAM algorithm, which has fast convergence rate and good accuracy. The inputs herein considered are the river width and depth, percent shade, view to sky, streamflow, and the minimum and maximum values of inputs required for uncertainty analysis. The results are presented as histograms for each input specifying the input's uncertainty. A comparison of the observational data with the DREAM algorithm estimates yielded a maximum error equal to 7.5%, which indicates excellent performance of the DREAM algorithm in ascertaining the effect of uncertainty in riverine water quality assessment.
In panel data analysis, predictors may impact response in substantially different manner. Some predictors are in homogenous effects across all individuals, while the others are in heterogenous way. How to effectively ...
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In panel data analysis, predictors may impact response in substantially different manner. Some predictors are in homogenous effects across all individuals, while the others are in heterogenous way. How to effectively differentiate these two kinds of predictors is crucial, particularly in high-dimensional panel data, since the number of parameters should be greatly reduced and hence lead to better interpretability by homogenous assumption. In this article, based on a hierarchical Bayesian panel regression model, we propose a novel yet effective Markov chain Monte Carlo (mcmc) algorithm together with a simple maximum ratio criterion to detect the predictors in homogenous effects in high-dimensional panel data. Extensive Monte Carlo simulations show that this mcmc algorithm performs well. The usefulness of the proposed method is further demonstrated by a real example from China financial market.
Financial time series contains information on market changes and investment risk fluctuations. This paper aims to study the volatility of China's stock market by analyzing the time series of Chinese stock market r...
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Financial time series contains information on market changes and investment risk fluctuations. This paper aims to study the volatility of China's stock market by analyzing the time series of Chinese stock market returns. We choose the closing price of the Shanghai and Shenzhen close index as a *** analyze the statistical characteristics of the time series, we choose the SVMN model in the SV model family by mcmc algorithm based on Gibbs sampling to estimate the basic characteristics of the stock market volatility. Through research, we find that China's stock market has the characteristics of agglomeration, volatility persistence and Leptokurtosis. We analyze these characteristics and specific market reasons to provide financial and securities service industries with relevant recommendations to stabilizing the market.
In this paper we consider Bayesian analysis of the possible changes in hydrological time series by Markov chain Monte Carlo(mcmc) algorithm. We consider multiple change-points and various possible situations. The appr...
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In this paper we consider Bayesian analysis of the possible changes in hydrological time series by Markov chain Monte Carlo(mcmc) algorithm. We consider multiple change-points and various possible situations. The approach of Bayesian stochastic search selection is used for detecting and estimating the number and positions of possible change-point in a piecewise constant model. mcmc algorithm is used to estimate the posterior distributions of parameters. The result of the analysis is applied to the hydrological data sets of the major river net area of Shunde in China and the data set of Nile River. In order to further investigate the trends in each segment of the hydrological data sets, we consider the analysis of change-point regression model via mcmc algorithm.
Independence among groups is assumed in traditional multilevel models. There is often spatial interaction between districts when data is grouped by geographical units. The individual will be influenced by adjacent reg...
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Independence among groups is assumed in traditional multilevel *** is often spatial interaction between districts when data is grouped by geographical *** individual will be influenced by adjacent regions,and assumpti...
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ISBN:
(纸本)9781479970162
Independence among groups is assumed in traditional multilevel *** is often spatial interaction between districts when data is grouped by geographical *** individual will be influenced by adjacent regions,and assumption of level-2 residual's distribution in traditional multilevel model will be *** statistical models are introduced into the multilevel model in order to deal with such spatial multilevel *** Bayesian inferences based on mcmc method for fixed effects,variance-covariance components and spatial regression parameters in improved multilevel model are given.
Bayesian analysis relies heavily on the Markov chain Monte Carlo (mcmc) algorithm to obtain random samples from posterior distributions. In this study, we compare the performance of mcmc stopping rules and provide a g...
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Bayesian analysis relies heavily on the Markov chain Monte Carlo (mcmc) algorithm to obtain random samples from posterior distributions. In this study, we compare the performance of mcmc stopping rules and provide a guideline for determining the termination point of the mcmc algorithm in latent variable models. In simulation studies, we examine the performance of four different mcmc stopping rules: potential scale reduction factor (PSRF), fixed-width stopping rule, Geweke's diagnostic, and effective sample size. Specifically, we evaluate these stopping rules in the context of the DINA model and the bifactor item response theory model, two commonly used latent variable models in educational and psychological measurement. Our simulation study findings suggest that single-chain approaches outperform multiple-chain approaches in terms of item parameter accuracy. However, when it comes to person parameter estimates, the effect of stopping rules diminishes. We caution against relying solely on the univariate PSRF, which is the most popular method, as it may terminate the algorithm prematurely and produce biased item parameter estimates if the cut-off value is not chosen carefully. Our research offers guidance to practitioners on choosing suitable stopping rules to improve the precision of the mcmc algorithm in models involving latent variables.
Efficient estimation for a stochastic volatility (SV) model has been actively pursued in recent years. In this paper, a new Markov chain Monte Carlo (mcmc) algorithm based on a combination of Kalman filtering and the ...
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Efficient estimation for a stochastic volatility (SV) model has been actively pursued in recent years. In this paper, a new Markov chain Monte Carlo (mcmc) algorithm based on a combination of Kalman filtering and the auxiliary sufficiency interweaving strategy (ASIS) is studied. Compared to other mcmc strategies like Stan algorithm ("Rstan") and the Gibbs algorithm ("R2Winbugs"), it is shown from finite-sample studies that the mcmc interweaving strategy improves both the estimation accuracy and the computation speed. We also applied the final selected algorithm, i.e., KMA5 algorithm, to the return rate of The Chinese CSI 300 Index (the Shanghai and Shenzhen 300 stock index in China), which further verifies the validity and accuracy of the new method.
Statistical simulation is one approach to problem solving without experimental testing. In this paper, a method for simulating the distribution of the Maxwell-Boltzmann distribution with mcmc approach by truncated Ray...
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Statistical simulation is one approach to problem solving without experimental testing. In this paper, a method for simulating the distribution of the Maxwell-Boltzmann distribution with mcmc approach by truncated Rayleigh distribution is presented and generated a random sample from this distribution by rejection sampling method. Some statistical inference properties for the parameter of the Maxwell-Boltzmann distribution such as maximum likelihood estimator, method of moments estimator, uniformly minimum variance unbiased estimator and minimum risk equivariant estimator, and the relationship between maximum likelihood estimator, uniformly minimum variance unbiased estimator, and also minimum risk equivariant estimator are found. Also, the hypothesis testing is discussed and the uniform most powerful test, generalized likelihood ratio test, uniformly most powerful unbiased test and uniformly most powerful invariant test and also confidence interval with equal tails, the shortest confidence interval, unbiased confidence interval and asymptotic confidence interval for the parameter of the Maxwell-Boltzmann model are found. By the way, a new method based on stochastic methods for finding the shortest and the unbiased confidence interval for the parameter of the Maxwell-Boltzmann model is introduced and it is shown that with a very close approximation, it leads to the same results of previous researches that are solved by numerical methods. It is proved that the Kullback-Leibler divergence between two Maxwell-Boltzmann distributions with different parameters is a convex function of the ratio of the parameters and then, the Hellinger distance between these two distributions is also calculated. By selecting the multiplicative group action, the discussion of invariance is followed and maximal invariant statistics and weakly equivariant estimators are found. Next, the uniformly most powerful invariant test critical region is performed using bootstrap. In the end, using two
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