非高斯脉冲噪声下的非线性自适应最小p范数迭代方法

稳定分布可以更好地描述实际应用中所遇到的具有显著脉冲特性的随机信号和噪声。与其它统计模型不同,α稳定分布没有统一闭式的概率密度函数,其二阶及二阶以上统计量均不存在。针对系统中存在独立SαS噪声与高斯噪声,本文提出了一种稳定分布环境下的非线性自适应最小p范数迭代算法。计算机模拟和分析表明,这种算法是一种在SαSG分布背景噪声条件下具有良好韧性的滤波方法。     

Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view

Abstract

This study examines the effects of supply chain agility (SCAG) and supply chain resilience (SCRES) on performance under the moderating effect of organizational culture. We have used the dynamic capability view (DCV) to conceptualize our theoretical models for different phases of humanitarian supply chain (HSC) (pre and post-disaster phases). We used partial least squares (PLS) to examine the proposed research hypotheses using 335 responses gathered from organizations in India using questionnaires designed for a single respondent. The results suggest that SCAG and SCRES are two important dynamic capabilities of supply chain, have significant effects on pre-disaster performance (PRE-DP). The control orientation does not have significant effect on the path joining SCAG and PRE-DP. However, the control orientation has a significant interaction effect on the path joining SCRES and PRE-DP. Similarly, SCRES has significant effect on post-disaster performance (POST-DP) but SCAG has no significant effect on POST-DP. In contrast, the flexible orientation has significant moderation effects on the paths SCAG/SCRES and POST-DP. These findings contribute to our understanding of the differential effect of SCAG/SCRES on supply chain performance in different contexts. The results provide further understanding to develop appropriate strategies for different phases. Finally, limitations of our study and future research are presented.     

A study on geographically weighted spatial autoregression models with spatial autoregressive disturbances

Abstract

Spatial heterogeneity and correlation are both considered in the geographical weighted spatial autoregressive model. At present, this kind of model has aroused the attention of some scholars. For the estimation of the model, the existing research is based on the assumption that the error terms are independent and identically distributed. In this article we use a computationally simple procedure for estimating the model with spatially autoregressive disturbance terms, both the estimates of constant coefficients and variable coefficients are obtained. Finally, we give the large sample properties of the estimators under some ordinary conditions. In addition, application study of the estimation methods involved will be further explored in a separate study.     

Improving interim decisions in randomized trials by exploiting information on short‐term endpoints and prognostic baseline covariates

Conditional power calculations are frequently used to guide the decision whether or not to stop a trial for futility or to modify planned sample size. These ignore the information in short‐term endpoints and baseline covariates, and thereby do not make fully efficient use of the information in the data. We therefore propose an interim decision procedure based on the conditional power approach which exploits the information contained in baseline covariates and short‐term endpoints. We will realize this by considering the estimation of the treatment effect at the interim analysis as a missing data problem. This problem is addressed by employing specific prediction models for the long‐term endpoint which enable the incorporation of baseline covariates and multiple short‐term endpoints. We show that the proposed procedure leads to an efficiency gain and a reduced sample size, without compromising the Type I error rate of the procedure, even when the adopted prediction models are misspecified. In particular, implementing our proposal in the conditional power approach enables earlier decisions relative to standard approaches, whilst controlling the probability of an incorrect decision. This time gain results in a lower expected number of recruited patients in case of stopping for futility, such that fewer patients receive the futile regimen. We explain how these methods can be used in adaptive designs with unblinded sample size re‐assessment based on the inverse normal P‐value combination method to control Type I error. We support the proposal by Monte Carlo simulations based on data from a real clinical trial.     

Bayesian non-crossing quantile regression for regularly varying distributions

Quantile regression is a very important statistical tool for predictive modelling and risk assessment. For many applications, conditional quantile at different levels are estimated separately. Consequently the monotonicity of conditional quantiles can be violated when quantile regression curves cross each other. In this paper, we propose a new Bayesian multiple quantile regression based on heavy tailed distribution for non-crossing. We consider a linear quantile regression model for simultaneous Bayesian estimation of multiple quantiles based on a regularly varying assumptions. The numerical and competitive performance of the proposed method is illustrated by simulation.     

Seamless phase II/III/IIIb clinical trial designs with different endpoints for different phases

Abstract

In a seamless phase II/III/IIIb trial, K (K?≥?2) doses versus placebo control are evaluated at phase II. Based on phase II results, one dose will be selected for phases III and IIIb. Pre-specified additional numbers of patients will be enrolled into the selected dose and placebo control during phases III and IIIb. Results of the phase III endpoint may be submitted for an early New Drug Application. Final analyses will be conducted for ultimate claims of treatment effects for the selected dose on the phase III and IIIb endpoints. Multiplicity adjustment is performed for the overall type I error rate control.     

非创始人CEO对企业盈余信息质量的影响——基于创业板公司的实证研究

企业内部组织结构的变化如何影响企业的盈余信息质量尚未得到充分的研究。民营企业在创业初期通常由创始人兼任企业CEO，但随着企业的发展壮大，由于创始人的个人能力和经验无法满足企业的发展需要，创始人会放弃兼任企业的CEO转而寻找外部职业经理人，由此产生第一类代理问题：创始人、外部监管部门和投资者逐渐产生对企业提供高质量盈余信息质量的需求。文章以2009—2016年我国A股创业板市场的336家民营上市公司为研究对象，以盈余持续性、盈余反应系数以及盈余管理程度测度盈余信息质量，运用两阶段最小二乘法对非创始人CEO和公司盈余信息质量之间的关系进行了实证研究。研究表明：创始人放弃兼任CEO显著提高了企业盈余信息质量；相对于企业内部非创始人股东担任CEO而言，来源于经理人市场的CEO对盈余信息质量的正向作用更强；当企业所面临的外部环境信息压力较大时，非创始人担任CEO对盈余信息质量的正向影响被削弱。文章的研究结果表明，企业在发展过程中组织结构的变化是企业提高盈余信息质量的动机之一，同时这种动机也会受到外部信息环境的影响。     

Parameter estimation and inference with spatial lags and cointegration

This article studies dynamic panel data models in which the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies a model with several cointegrating relationships that, unlike in the standard case, are nonlinear in the coe?cients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator under a set of low-level assumptions. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, as well as firm-specific and industry data. We construct the economic space using a “closeness” measure for firms based on input–output matrices. Our estimates show that this particular form of spatial correlation of credit default swap spreads is substantial and highly significant.     

Estimation of causal continuous‐time autoregressive moving average random fields

We estimate model parameters of Lévy‐driven causal continuous‐time autoregressive moving average random fields by fitting the empirical variogram to the theoretical counterpart using a weighted least squares (WLS) approach. Subsequent to deriving asymptotic results for the variogram estimator, we show strong consistency and asymptotic normality of the parameter estimator. Furthermore, we conduct a simulation study to assess the quality of the WLS estimator for finite samples. For the simulation, we utilize numerical approximation schemes based on truncation and discretization of stochastic integrals and we analyze the associated simulation errors in detail. Finally, we apply our results to real data of the cosmic microwave background.