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.     

随机变量投影的一种应用

利用随机变量的投影关系,定义了偏方差矩阵,从而导出了逆方差阵元素的一种形式在随机变量是正态的条件,它为判别条件独立性有方便的操作办法。     

Performance of weather-conditional rebates under different risk preferences

Retailers who sell seasonal products often face challenges in demand management due to weather uncertainty. In many cases, they make their ordering and pricing decisions prior to the regular selling season but the vast majority of sales do not occur until after the season starts, during which unfavorable weather conditions may result in high monetary losses. To protect against such adverse financial outcomes, retailers may offer weather-linked promotions such as weather rebates and induce customers to make early purchases. Specifically, weather-conditional rebates are incentives offered in an advance promotional period to be paid to the early buyers if the weather state in the regular season is unfavorable. In the presence of seasonal weather uncertainty, risk attitudes of retailers and buyers may play an important role on the effectiveness of these promotions. In this paper, we analyze the performance of weather-conditional rebates by explicitly considering the impact of different risk behaviors. First, we study the case in which the retailer and customers are risk-neutral and show that the weather-conditional rebates are effective in increasing the retailer's profits. Under the assumption of the retailer's risk-neutrality, we conduct a simulation study to investigate the impact of customers' alternative early-purchase behaviors on the performance of the rebate program. Next, we consider a risk-averse retailer. We model the retailer's risk aversion primarily in the mean–variance framework and find that the rebate program can be designed to increase the mean profit and reduce the profit variance simultaneously. Furthermore, by combining the rebate program with a financial instrument such as binary weather options, the retailer can obtain greater benefits from weather-conditional rebates.     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

Leader negative feedback-seeking and leader effectiveness in leader-subordinate relationships: The paradoxical role of subordinate expertise

From a motivational perspective of feedback-seeking behavior, we examined the mediating role of leaders' negative feedback-seeking from subordinates in the relationship between the quality of leader-member exchange (LMX) and subordinates' evaluation of leader effectiveness, along with the moderating role of subordinate expertise in the mediated relationship. Using 151 unique matched sets of leader and subordinate reports obtained from 5 large Korean companies, we found that the positive relationship between LMX and leader effectiveness was mediated by leaders' negative feedback-seeking. Additionally, the positive relationship between LMX and leader negative feedback-seeking was stronger when perceived subordinate expertise was lower. Lastly, the indirect effect of LMX on leader effectiveness through leader negative feedback-seeking was stronger when perceived subordinate expertise was lower. These findings were obtained after controlling for leaders' power distance and goal orientations that might influence their motives to seek or avoid feedback. Theoretical and practical implications are discussed.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

Conditional variance estimation in heteroscedastic regression models

First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique to improve the inference for the conditional variance. The proposed methods are investigated through their asymptotic distributions and numerical performances.