ASYMPTOTIC LIKELIHOOD APPROXIMATIONS USING A PARTIAL LAPLACE APPROXIMATION

Elimination of a nuisance variable is often non‐trivial and may involve the evaluation of an intractable integral. One approach to evaluate these integrals is to use the Laplace approximation. This paper concentrates on a new approximation, called the partial Laplace approximation, that is useful when the integrand can be partitioned into two multiplicative disjoint functions. The technique is applied to the linear mixed model and shows that the approximate likelihood obtained can be partitioned to provide a conditional likelihood for the location parameters and a marginal likelihood for the scale parameters equivalent to restricted maximum likelihood (REML). Similarly, the partial Laplace approximation is applied to the t‐distribution to obtain an approximate REML for the scale parameter. A simulation study reveals that, in comparison to maximum likelihood, the scale parameter estimates of the t‐distribution obtained from the approximate REML show reduced bias.     

A structured variational learning approach for switching latent factor models

A data-driven approach for modeling volatility dynamics and co-movements in financial markets is introduced. Special emphasis is given to multivariate conditionally heteroscedastic factor models in which the volatilities of the latent factors depend on their past values, and the parameters are driven by regime switching in a latent state variable. We propose an innovative indirect estimation method based on the generalized EM algorithm principle combined with a structured variational approach that can handle models with large cross-sectional dimensions. Extensive Monte Carlo simulations and preliminary experiments with financial data show promising results.     

关于Gauss-Weierstrass算子线性组合的同时逼近

为了提高正线性算子 Gauss-Weierstrass 算子的逼近阶,往往采用线性组合的方法.本文主要研究了一类 Gauss-Weierstrass 算子线性组合的同时逼近问题,在一致逼近的意义下,给出了逼近的正定理、逆定理及特征刻划.即我们得到了如下结果:设 f∈C_(-∞,+∞),f~(m)(x)存在,W_(n,r)(f;x)表示 Gauss-Weierstrass 算子的一种线性组合,则当 a<2r 时,有(i)‖W_(n,r)~(m)(f;x)-f~(m)‖≤M[ω_(2r)(fn~(-1/2))+n~(-r)];(ii) k_(2r)(f~(m);n~(-r))≤‖W_(k,r)~(m)(f;x)-f_(x)~(m)‖+M(k/n)~rk_(2r)(f~(m);k~(-r));(iii)‖W_(n,r)~(m)(f;x)-f~(m)‖=O(n~(-(a/2))ω_(2r)(f~(m);h)=O(h~a).     

The effects of different choices of order for autoregressive approximation on the Gaussian likelihood estimates for ARMA models

This paper investigates, by means of Monte Carlo simulation, the effects of different choices of order for autoregressive approximation on the fully efficient parameter estimates for autoregressive moving average models. Four order selection criteria, AIC, BIC, HQ and PKK, were compared and different model structures with varying sample sizes were used to contrast the performance of the criteria. Some asymptotic results which provide a useful guide for assessing the performance of these criteria are presented. The results of this comparison show that there are marked differences in the accuracy implied using these alternative criteria in small sample situations and that it is preferable to apply BIC criterion, which leads to greater precision of Gaussian likelihood estimates, in such cases. Implications of the findings of this study for the estimation of time series models are highlighted.     

Confidence Intervals Based on Local Linear Smoother

Point-wise confidence intervals for a non-parametric regression function in conjunction with the popular local linear smoother are considered. The confidence intervals are based on the asymptotic normal distribution of the local linear smoother. Their coverage accuracy is evaluated by developing Edgeworth expansion for the coverage probability. It is found that the coverage error near the boundary of the support of the regression function is of a larger order than that in the interior, which implies that the local linear smoother is not adaptive to the boundary in terms of coverage. This is quite unexpected as the local linear smoother is adaptive to the boundary in terms of the mean squared error.     

On uniform approximation of Poisson variates by normal ones

In this paper the accuracy of the normal approximation to the Poisson is treated from the viewpoint of direct approximation of Poisson variables by normal ones. The conclusions that are derived on the accuracy of this approximation lead (among others) to very useful results on confidence limits for the mean of a linear combination of independent Poisson variables; these latter are employed in precise determination of the composition of a mixture of radioactive isotopes by means of a scintillation counter.     

Parameter estimation in a verhulst stochastic model

Summary Maximum likelihood estimation is a well-known statistical tool. When applied to the study of dynamical continuous-time phenomena, it requires some specific assumptions. In this paper we discuss some properties of the maximum likelihood estimator for a stochastic Verhulst model and we show some simulation results on the behaviour of the corresponding discrete estimator.     

天线罩覆盖的波束近轴场

本文根据复射线法对通过介质天线罩的高斯波束场进行近轴近似分析。实空间的轴向复射线追综大大简化了计算过程,而复相位差、反射-传输系数和曲面扩散系数的进一步校正则提供了相当精确的结果。所得数字结果表明,在波束的近轴区域内为获得同样的计算精度,近轴近似法所需要的计算机时间远小于复射线追综法的时间。     

Approximation of power in multivariate analysis

We consider the calculation of power functions in classical multivariate analysis. In this context, power can be expressed in terms of tail probabilities of certain noncentral distributions. The necessary noncentral distribution theory was developed between the 1940s and 1970s by a number of authors. However, tractable methods for calculating the relevant probabilities have been lacking. In this paper we present simple yet extremely accurate saddlepoint approximations to power functions associated with the following classical test statistics: the likelihood ratio statistic for testing the general linear hypothesis in MANOVA; the likelihood ratio statistic for testing block independence; and Bartlett's modified likelihood ratio statistic for testing equality of covariance matrices.     

关于Ismail-May算子线性组合的同时逼近

本文推广了的结果,主要研究了一类Ismail-May算子线性组合的同时逼近问题,在一致通近意义下,给出了逼近的正定理、逆定理及非最优逼近阶的特征刻划.