Modified explicit inversive congruential pseudorandom numbers with power of 2 modulus

A modified version of the explicit inversive congruential method with power of 2 modulus for generating uniform pseudorandom numbers is introduced. The statistical independence behaviour of the generated sequences is studied based on the distribution of all pairs of successive pseudorandom numbers over the entire period. Lower and upper bounds for the discrepancy of the corresponding two-dimensional point sets are established. These results certainly play only a minor part in studying the statistical independence behaviour of the generated sequences, but they show that modified explicit inversive congruential pseudorandom numbers have some attractive properties at least regarding their two-dimensional discrepancy. The method of proof relies heavily on a thorough analysis of certain exponential sums.     

A non-linear congruential pseudo random number generator

A non-linear congruential pseudo random number generator is introduced. This generator does not have the lattice structure in the distribution of tuples of consecutive pseudo random numbers which appears in the case of linear congruential generators. A theorem on the period length of sequences produced by this type of generators is proved. This theorem justifies an algorithm to determine the period length. Finally a simulation problem is described where a linear congruential generator produces completely useless results whereas good results are obtained if a non-linear congruential generator of about the same period length is applied.     

Long-range correlation analysis of the Wichmann-Hill random number generator

The distribution of points (r _n,r _n+s), n = 0, 1, 2,...whose coordinates are terms at distance s of the pseudorandom sequence generated by the Wichmann and Hill method is studied. It is known that for many congruential generators critical values of the distance s exist such that these points, far from being uniformly distributed, are concentrated on very few lines. An algorithm is described for computing the critical distances within the Wichmann-Hill sequence and the results obtained are compared with those of other linear congruential generators.     

Normal random numbers lying on spirals and clubs

Two methods for transforming uniformly distributed random numbers into normally distributed random numbers are considered in conjunction with linear congruential generators. The two-dimensional lattice structure of the uniform random numbers is transformed by the Box-Muller method into a spiral structure and by the polar method into a club-shaped structure. The approximation of the two-dimensional normal distribution and the independence of the associated random variables are discussed.     

Cross-correlation between simultaneously generated sequences of pseudo-random uniform deviates

Systematic patterns are revealed when sequences of pseudo-random uniform deviates are generated from multiplicative congruential generators. If the initial seeds, x ₀ and y ₀, for two such sequences are related by y ₀ = (n ₁/n ₂)x ₀, where n ₁ and n ₂ are relatively prime, positive integers, then an approximate argument suggests that the asymptotic correlation coefficient between corresponding members of the two sequences is (n ₁ n ₂)^–1. This unsettling phenomenon is discussed in the context of related, existing literature.     

An exhaustive analysis of prime modulus multiplicative congruential random number generators with modulus smaller than 215

This paper presents the results of an exhaustive analysis of all of the prime modulus multiplicative congruential random number (RN) generators with moduli smaller than 2¹⁵. In an amount of around 20 million multipliers which are able to produce a full period of RNs, 239 multipliers have a good lattice structure. Among which 52 multipliers further pass a comprehensive battery of empirical tests. These 52 multipliers thus possess good local and global statistical properties. It is worthwhile to note that some empirically tested multipliers recommended in some previous studies are not on this list. The conclusion is that both theoretical and empirical tests are mandatory to sieve out good multipliers. To generate RNs of very long period, many existing techniques can be applied without further effort.     

A multiple stream generator based on de Bruijn digraph homomorphisms

We propose a method to obtain several streams of pseudorandom numbers based on a backbone generator of the generalized shift register type. The method is based on inverting one cycle in a de Bruijn digraph into many sequences in a higher-order de Bruijn graph via an appropriate graph homomorphism. We apply this technique to twisted generalized feedback shift register generators and to the Mersenne Twister MT19937. Positive results of statistical testing are reported.     

The pseudo-random number generators in Commodore and Apple microcomputers

In this paper the pseudo-random number generators which are implemented in the widely used Commondore and Apple microcomputers are discussed. The results of this investigation show that these generators are not useful for scientific work. In particular short periods appear in the sequences of the generated pseudo-random numbers.     

Statistical Monitoring of Autocorrelated Simple Linear Profiles Based on Principal Components Analysis

In this article, a transformation method using the principal component analysis approach is first applied to remove the existing autocorrelation within each profile in Phase I monitoring of autocorrelated simple linear profiles. This easy-to-use approach is independent of the autocorrelation coefficient. Moreover, since it is a model-free method, it can be used for Phase I monitoring procedures. Then, five control schemes are proposed to monitor the parameters of the profile with uncorrelated error terms. The performances of the proposed control charts are evaluated and are compared through simulation experiments based on different values of autocorrelation coefficient as well as different shift scenarios in the parameters of the profile in terms of probability of receiving an out-of-control signal.     

Chaos: But Not in Both Directions!

This paper will informally explore the reversal of some stochastic autoregressive processes, which lead to deterministically chaotic processes. Correspondingly, the stochastic reversal of map models is shown to lead to a new class of invariant distribution. Finally, some connections between congruential recursions and independence in discretized chaotic processes are illustrated.     

基于自相关视角的弱平稳过程之间的伪回归分析

随机干扰项之间的未知形式自相关是导致相互独立的弱平稳过程之间伪回归的主要原因.通过理论分析和一系列的蒙特卡罗模拟,揭示了数据过程本身的持久性、样本容量T和随机干扰项自相关之间的内在联系.研究发现随机干扰项往往呈现出与数据过程阶数相同的自相关.进一步研究表明,运用广义差分法和Cochrane- Orcutt迭代法虽然能大大减少伪回归概率,但在有些情况下,即使当样本容量较大时,较高阶的Cochrane- Orcutt迭代法仍然无法避免伪回归的发生.     

Estimating Spatial Autocorrelation With Sampled Network Data

Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc.). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so, network relationships (i.e., edges) involving unsampled nodes are overlooked. This leads to distorted network structure and underestimated spatial autocorrelation. To solve the problem, we propose here a novel solution. By temporarily assuming that the spatial autocorrelation is small, we are able to approximate the likelihood function by its first-order Taylor’s expansion. This leads to the method of approximate maximum likelihood estimator (AMLE), which further inspires the development of paired maximum likelihood estimator (PMLE). Compared with AMLE, PMLE is computationally superior and thus is particularly useful for large-scale network data analysis. Under appropriate regularity conditions (without assuming a small spatial autocorrelation), we show theoretically that PMLE is consistent and asymptotically normal. Numerical studies based on both simulated and real datasets are presented for illustration purpose.     

Detection of a changepoint,a mean-shift accompanied with a trend change,in short time-series with autocorrelation

In this study, a changepoint model, which can detect either a mean shift or a trend change when accounting for autocorrelation in short time-series, was investigated with simulations and a new method is proposed. The changepoint hypotheses were tested using a likelihood ratio test. The test statistic does not follow a known distribution and depends on the length of the time-series and the autocorrelation. The results imply that it is not possible to detect autocorrelation and that the estimate of the autocorrelation parameter is biased. It is therefore recommended to use critical values from the empirical distribution for a fixed autocorrelation.     

Empirical likelihood methods for discretely observed Gaussian moving averages

This paper is concerned with parametric estimation, model specification and autocorrelation diagnosis for stationary moving averages driven by a Wiener process. By incorporating the analysis of the spectral densities of the discretely observed trajectory, empirical likelihood methods based on moment conditions are developed to the dependent sequences in this paper for estimation and test. Theoretical properties of the empirical likelihood estimator for parameters are provided. Empirical likelihood ratio tests for model specification of the moving averages are proposed by means of the bootstrap strategy. Simulation and empirical case studies are carried out to confirm the effectiveness of the proposed estimation and test.     

An Application of Nonlinear Time Series Forecasting

By means of a real application, it is seen how ARIMA forecasts can be improved when nonlinearities are present. The autocorrelation function (ACF) of the squared residuals provides a convenient tool to check the linearity assumption. Once nonlinearity has been detected, parsimonious bilinear processes seem rather adequate to model it. The detection of nonlinearity and the forecast improvement appear to be rather robust with respect to changes in the linear and bilinear specification. Finally, what bilinear models seem to capture are periods of atypical behavior or sequences of outliers.     

CHANGE-OVER DESIGNS WITH ERRORS FOLLOWING A FIRST ORDER AUTOREGRESSIVE PROCESS

This paper deals with the problem of analysing the change over design in the context of a first order autoregressive process for the error terms. The method of maximum likelihood has been adopted for estimating treatment effects. The conditions derived for obtaining a balanced change over design show that a change over design balanced in the absence of autocorrelation is not necessarily balanced in the presence of autocorrelation. Also, it is observed that the autocorrelation co-efficient and the treatment effect when p≠0 can be tested as usual with the likelihood ratio test criterion.     

Application of Quasi-Least Squares to Analyse Replicated Autoregressive Time Series Regression Models

Time series regression models have been widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this paper, we study the application of the quasi-least squares method to estimate the parameters in a replicated time series model with errors that follow an autoregressive process of order p. We also discuss two other established methods for estimating the parameters: maximum likelihood assuming normality and the Yule-Walker method. When the number of repeated measurements is bounded and the number of replications n goes to infinity, the regression and the autocorrelation parameters are consistent and asymptotically normal for all three methods of estimation. Basically, the three methods estimate the regression parameter efficiently and differ in how they estimate the autocorrelation. When p=2, for normal data we use simulations to show that the quasi-least squares estimate of the autocorrelation is undoubtedly better than the Yule-Walker estimate. And the former estimate is as good as the maximum likelihood estimate almost over the entire parameter space.     

Optimal designs for multivariate logistic mixed models with longitudinal data

This paper considers the optimal design problem for multivariate mixed-effects logistic models with longitudinal data. A decomposition method of the binary outcome and the penalized quasi-likelihood are used to obtain the information matrix. The D-optimality criterion based on the approximate information matrix is minimized under different cost constraints. The results show that the autocorrelation coefficient plays a significant role in the design. To overcome the dependence of the D-optimal designs on the unknown fixed-effects parameters, the Bayesian D-optimality criterion is proposed. The relative efficiencies of designs reveal that both the cost ratio and autocorrelation coefficient play an important role in the optimal designs.     

Rates of convergence of autocorrelation estimates for periodically correlated autoregressive Hilbertian processes

Autoregressive Hilbertian (ARH) processes are of great importance in the analysis of functional time series data and estimation of the autocorrelation operators attracts the attention of various researchers. In this paper, we study estimators of the autocorrelation operators of periodically correlated autoregressive Hilbertian processes of order one (PCARH(1)), which is an extension of ARH(1) processes. The estimation method is based on the spectral decomposition of the covariance operator and considers two main cases: known and unknown eigenvectors. We show the consistency in the mean integrated quadratic sense of the estimators of the autocorrelation operators and present upper bounds for the corresponding rates.     

中国季度GDP的季节调整：结构时间序列方法

 内容提要：本文利用结构时间序列方法讨论了中国季度GDP的季节调整问题,从季节单位根、季节自相关、周期自相关等多个方面对不同季节模式的调整结果进行了比较。结论认为,随机虚拟变量形式和三角函数形式得到的调整结果非常相似;结构时间序列方法更好地捕捉到了时变季节特征,明显优于X-11和SEATS方法;非高斯稳健季节调整的结果表明,高斯结构时间序列方法具有较好的稳定性。