A robust QTL mapping procedure

In quantitative trait linkage studies using experimental crosses, the conventional normal location-shift model or other parameterizations may be unnecessarily restrictive. We generalize the mapping problem to a genuine nonparametric setup and provide a robust estimation procedure for the situation where the underlying phenotype distributions are completely unspecified. Classical Wilcoxon–Mann–Whitney statistics are employed for point and interval estimation of QTL positions and effects.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.     

论信托关系中委托人对受托人的请求权——兼论信托的合同基础

在信托制度中是存在着合同基础的,赋予委托人对受托人的请求权只是归还了其本应享有的权利,委托人享有对受托人的请求权是应被肯定的,这仅是还原了信托的本来面貌,并没有扭曲信托制度。     

The Tunnel at the End of the Light: the Crisis of Transition in Hungary

The economic, political and social transition processes of the last two decades, far from being in harmony, clearly have a detrimental rather than an enriching influence on each other. These processes will never become harmonised just by themselves in a natural fashion. The worst obstacle of a successful transformation is that the chief actors, such as the government, the market and the representatives of the society. The Prince, the Merchant and the Citizen do not show the necessary commitment, competence and determination to create a new, effective type of partnership. This is a challenge which urgently demands answers on all levels of governance, economic and social life. This must be done in the frames of local communities, and on the regional, the European and the global levels. Nearly twenty years after the political turnabout, Hungary now needs a new social contract and must lay down the ethical, political, institutional and intellectual foundations for its twenty-first century democracy.

C2C网上交易中信号机制的有效性分析

本文应用Berry等(1995)^[2]提出的离散选择需求模型和差异产品的伯川德竞争模型,并利用淘宝网上的交易数据,对声誉、消费者保障计划、保修服务及信息披露四种信号策略在网上交易中的作用进行了实证研究。需求估计结果表明消费者保障计划中的"7天无理由退换货"计划以及保修服务可以作为产品质量的信号;而在其他有效信号策略存在的情况下,消费者保障计划中的"先行赔付"计划以及卖家声誉作为质量信号的作用则被削弱。成本分析表明,信息披露虽然可以提高消费者购买的可能性,但是由于发出信号的成本太低,容易被低质量卖家所模仿,因此并不能作为有效的质量信号。本文首次从供给和需求两个方面分析了信号机制在信息不对称情况下的效应及其作用机制。     

Generalized Kerridge’s inaccuracy measure for conditionally specified models

Recently, conditional Renyi’s divergence of order α and Kerridge’s inaccuracy measures are studied by Navarro et al. (2014 Navarro, J., Sunoj, S.M., Linu, M.N. (2014). Characterizations of bivariate models using some dynamic conditional information divergence measures. Commun. Stat. Theory Methods 43:1939–1948.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). In the present article, a generalized dynamic conditional Kerridge’s inaccuracy measure is introduced, which can be represented as the sum of conditional Renyi’s divergence and Renyi’s entropy. Some useful bounds are obtained using the concept of likelihood ratio order. The results are extended to weighted distributions. Sufficient conditions are obtained for the monotonicity of the proposed measure. Characterizations for bivariate exponential conditional distribution are presented based on the proposed measure.     

Trend estimation of multivariate time series with controlled smoothness

This paper extends the univariate time series smoothing approach provided by penalized least squares to a multivariate setting, thus allowing for joint estimation of several time series trends. The theoretical results are valid for the general multivariate case, but particular emphasis is placed on the bivariate situation from an applied point of view. The proposal is based on a vector signal-plus-noise representation of the observed data that requires the first two sample moments and specifying only one smoothing constant. A measure of the amount of smoothness of an estimated trend is introduced so that an analyst can set in advance a desired percentage of smoothness to be achieved by the trend estimate. The required smoothing constant is determined by the chosen percentage of smoothness. Closed form expressions for the smoothed estimated vector and its variance-covariance matrix are derived from a straightforward application of generalized least squares, thus providing best linear unbiased estimates for the trends. A detailed algorithm applicable for estimating bivariate time series trends is also presented and justified. The theoretical results are supported by a simulation study and two real applications. One corresponds to Mexican and US macroeconomic data within the context of business cycle analysis, and the other one to environmental data pertaining to a monitored site in Scotland.     

Generalized empirical likelihood inference in partially linear model for longitudinal data with missing response variables and error-prone covariates

In this article, we consider statistical inference for longitudinal partial linear models when the response variable is sometimes missing with missingness probability depending on the covariate that is measured with error. A generalized empirical likelihood (GEL) method is proposed by combining correction attenuation and quadratic inference functions. The method that takes into consideration the correlation within groups is used to estimate the regression coefficients. Furthermore, residual-adjusted empirical likelihood (EL) is employed for estimating the baseline function so that undersmoothing is avoided. The empirical log-likelihood ratios are proven to be asymptotically Chi-squared, and the corresponding confidence regions for the parameters of interest are then constructed. Compared with methods based on NAs, the GEL does not require consistent estimators for the asymptotic variance and bias. The numerical study is conducted to compare the performance of the EL and the normal approximation-based method, and a real example is analysed.