Goodness‐of‐fit methods for matched case‐control studies

The authors propose graphical and numerical methods for checking the adequacy of the logistic regression model for matched case‐control data. Their approach is based on the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a centered Gaussian limit whose distribution can be approximated via computer simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a certain number of simulated realizations of the approximate limiting process under the null hypothesis. The proposed techniques allow one to check the functional form of each covariate, the logistic link function as well as the overall model adequacy. The authors assess the performance of the proposed methods through simulation studies and illustrate them using data from a cardiovascular study.     

Testing Inference in Inflated Beta Regressions under Model Misspecification

We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quasi-z testing inference can be considerably more accurate than inference made through the usual z tests, especially when there is model misspecification. Interval estimation is also considered. We also present an empirical application that uses real (not simulated) data.     

The exact properties of the lawless and wang operational ridge regression estimator in a misspecified regression equation

The exact properties of the Lawless and Wang Operational Ridge Regression estimator are derived in the context of a misspecified regression equation.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Specification and testing of multiplicative time-varying GARCH models with applications

In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based on statistical inference is presented. It is heavily dependent on Lagrange multiplier type misspecification tests. The tests are easily implemented as they are entirely based on auxiliary regressions. Finite-sample properties of the strategy and tests are examined by simulation. The modelling strategy is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns.     

Choice of Parametric Accelerated Life and Proportional Hazards Models for Survival Data: Asymptotic Results

We discuss the impact of misspecifying fully parametric proportional hazards and accelerated life models. For the uncensored case, misspecified accelerated life models give asymptotically unbiased estimates of covariate effect, but the shape and scale parameters depend on the misspecification. The covariate, shape and scale parameters differ in the censored case. Parametric proportional hazards models do not have a sound justification for general use: estimates from misspecified models can be very biased, and misleading results for the shape of the hazard function can arise. Misspecified survival functions are more biased at the extremes than the centre. Asymptotic and first order results are compared. If a model is misspecified, the size of Wald tests will be underestimated. Use of the sandwich estimator of standard error gives tests of the correct size, but misspecification leads to a loss of power. Accelerated life models are more robust to misspecification because of their log-linear form. In preliminary data analysis, practitioners should investigate proportional hazards and accelerated life models; software is readily available for several such models.     

金融计量新方法——有效矩方法理论研究进展

由Gallant，Tauchen（1996），Gallant，Long（1997）发展并完善的有效矩方法（Efficient Method of Moments，简记为EMM）是现代金融理论中模型设定分析的有效方法。本文对EMM理论研究进展进行了综述并指出了其中还存在的一些不完善和值得继续研究的地方。     

Estimates of the quantiles of kendall's partial rank correlation coefficient

The sampling distribution of kendall's partial rank correlation coefficient, J_xy?z, is not known for N>4, where N is the number of subjectts. Moran (1951) used a direcr conbinatorial method to obtain the distribution of J_xy?z forN=4; however, ten minor computationa; errors in his Table 2apparently resulted in how erroneous entries for his frequency table. Since the parctial limits of the direct combinatorial approach have been reached once N>4, the first main objective of this paper was to obtain the exact distribution of J_xy?z for N=f, 6, and 7 using an electronic computer. The second was to use the Monte Carlo method to obtain reliable estimates of the quantiles of J_xy?z for N=8,9,...,30     

Marginal Regression for Binary Longitudinal Data in Adaptive Clinical Trials

Abstract.  In an adaptive clinical trial research, it is common to use certain data-dependent design weights to assign individuals to treatments so that more study subjects are assigned to the better treatment. These design weights must also be used for consistent estimation of the treatment effects as well as the effects of the other prognostic factors. In practice, there are however situations where it may be necessary to collect binary responses repeatedly from an individual over a period of time and to obtain consistent estimates for the treatment effect as well as the effects of the other covariates in such a binary longitudinal set up. In this paper, we introduce a binary response-based longitudinal adaptive design for the allocation of individuals to a better treatment and propose a weighted generalized quasi-likelihood approach for the consistent and efficient estimation of the regression parameters including the treatment effects.