A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances

This article considers the twin problems of testing for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH disturbances in the linear regression model. A feature of these testing problems, ignored by the standard Lagrange multiplier test, is that they are onesided in nature. A test that exploits this one-sided aspect is constructed based on the sum of the scores. The small-sample-size and power properties of two versions of this test under both normal and leptokurtic disturbances are investigated via a Monte Carlo experiment. The results indicate that both versions of the new test typically have superior power to two versions of the Lagrange multiplier test and possibly also more accurate asymptotic critical values.     

某自锚式悬索桥减震性能研究

以某自锚式悬索桥为研究对象，采用非线性时程分析方法，对整体式减隔震装置铅芯橡胶支座对自锚式悬索桥的减震性能进行研究。通过对在辅助墩上布置铅芯橡胶支座和普通橡胶支座的自锚式悬索桥的抗震性能对比分析，得到在辅助墩上布置铅芯橡胶支座对自锚式悬索桥的地震效应减震效果明显的结论。     

桥梁横向地震碰撞响应研究进展

在强震作用下，桥梁上、下部结构由于动力特性的不同而发生不同相振动，从而引起梁体与挡块间的非线性碰撞。汶川大地震中，大量公路桥梁遭到严重破坏，横向抗震挡块破坏尤其严重，其中部分是由于横向碰撞而引起。针对这种碰撞现象，对正交简支梁桥、连续梁桥、斜交桥及高速铁路桥梁的横向碰撞反应进行了回顾和总结，并介绍了横桥向防撞措施及震后加固技术的研究现状，同时指出目前我国在这方面亟需解决的问题和进一步研究的方向。     

An LM-type test for idiosyncratic unit roots in the exact factor model with integrated factors

We consider an exact factor model with integrated factors and propose an LM-type test for unit roots in the idiosyncratic component. We show that, for a fixed number of panel individuals (N) and when the number of time points (T) tends to infinity, the limiting distribution of the LM-type statistic is a weighted sum of independent Chi-square variables with one degree of freedom, and when T tends to infinity followed by N tending to infinity, the limiting distribution is standard normal. The results should contribute to the challenging task of deriving likelihood-based unit-root tests in dynamic factor models.     

Testing for normally in censored regressions

This paper derives a Lagrange Multiplier test for normality in censored regressions. The test is derived against the generalized log-gamma distribution, in which normal is a special case. The resulting test statistic coincides to some extent with previously suggested score and conditional moment tests. Estimation of the variance is performed by using the matrix of second order derivatives in order to get an easy to use test statistic. Small sample performance of the test is studied and compared to other tests by Monte Carlo experiments.     

DOUBLE LENGTH ARTIFICIAL REGRESSIONS FOR TESTING SPATIAL DEPENDENCE

This paper derives two simple artificial Double Length Regressions (DLR) to test for spatial dependence. The first DLR tests for spatial lag dependence while the second DLR tests for spatial error dependence. Both artificial regressions utilize only least squares residuals of the restricted model and are therefore easy to compute. These tests are illustrated using two simple examples. In addition, Monte Carlo experiments are performed to study the small sample performance of these tests. As expected, these DLR tests have similar performance to their corresponding LM counterparts.     

The effects of outliers on two nonlinearity tests

Two Lagrange multiplier tests for time series nonlinearities in the presence of outliers are examined by simulation experiments. The nonlinearities studied are autoregressive conditional heteroskedasticity (ARCH) and bilinearity; the outlier types are additive, innovative, temporary change and reallocation outliers. The results show that both the sizes and powers of the tests can be severely distorted by even a single outlier. The severity of the distortions depends on the outlier type and magnitude, but also on the underlying process generating 'the series.     

AN EXTENSION OF THE EM ALGORITHM FOR OPTIMIZATION OF CONSTRAINED LIKELIHOOD: AN APPLICATION IN TOXICOLOGY

Since its introduction in the mid 1970's, the EM algorithm has found a widespread popularity for solving the likelihood equations. Several investigators have used the algorithm in a variety of problems with incomplete information to obtain maximum likelihood estimates in numerous applications. The algorithm, however, becomes inappropriate when the underlying equations are subject to some constraints. Although an extension has been proposed to derive solutions when the parameters are subject to a set of linear constraints, the evaluation of likelihood equations from incomplete data when the equations are subject to a nonlinear constraint is still an open problem. Here, we consider a mixture model, a classical example of incomplete data, and discuss the problem of maximum likelihood estimation of the model parameters when the parameters have to satisfy a nonlinear constraint. An extension of the EM algorithm based on the celebrated Lagrange multiplier will be proposed to solve the equations. An application of the methodology in animal bioassay experiments for risk assessment of toxic substances will be described and data from a toxicological experiment will be used to illustrate the results.