State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.     

Robust signed-rank estimation and variable selection for semi-parametric additive partial linear models

A fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known. This becomes even more challenging when the data contain gross outliers or unusual observations. However, in practice the true covariates are not known in advance, nor is the smoothness of the functional form. A robust model selection approach through which we can choose the relevant covariates components and estimate the smoothing function may represent an appealing tool to the solution. A weighted signed-rank estimation and variable selection under the adaptive lasso for semi-parametric partial additive models is considered in this paper. B-spline is used to estimate the unknown additive nonparametric function. It is shown that despite using B-spline to estimate the unknown additive nonparametric function, the proposed estimator has an oracle property. The robustness of the weighted signed-rank approach for data with heavy-tail, contaminated errors, and data containing high-leverage points are validated via finite sample simulations. A practical application to an economic study is provided using an updated Canadian household gasoline consumption data.     

Non-tariff barrier on chicken imports into Russia: Impact on production,trade and prices

We analyze the Russian restriction on chicken imports as an example of a non-tariff barrier (NTB). Cointegration test results support the hypothesis that the NTB limited trade. We calibrate an equilibrium market model to cointegration results to assess the market impact. The tariff equivalent of the Russian chicken NTB is estimated to be 30 − 40% depending on the representation of consumer demand. Removing the NTB decreases domestic production by 4–5% and domestic price by 27%–34%, while imports increase by 326–423 thousand tons annually in 2015–2019.     

A new regression model for bimodal data and applications in agriculture

We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.     

Modified maximum likelihood estimator under the Jones and Faddy's skew t-error distribution for censored regression model

It is well-known that classical Tobit estimator of the parameters of the censored regression (CR) model is inefficient in case of non-normal error terms. In this paper, we propose to use the modified maximum likelihood (MML) estimator under the Jones and Faddy''s skew t-error distribution, which covers a wide range of skew and symmetric distributions, for the CR model. The MML estimators, providing an alternative to the Tobit estimator, are explicitly expressed and they are asymptotically equivalent to the maximum likelihood estimator. A simulation study is conducted to compare the efficiencies of the MML estimators with the classical estimators such as the ordinary least squares, Tobit, censored least absolute deviations and symmetrically trimmed least squares estimators. The results of the simulation study show that the MML estimators work well among the others with respect to the root mean square error criterion for the CR model. A real life example is also provided to show the suitability of the MML methodology.     

中国地方政府创新模式的可持续研究 ——基于“中国政府创新最佳实践”案例（2011—2016）的QCA方法

基于2011—2016年三届“中国政府创新最佳实践”的30个案例,引入定性比较分析（QCA）,从中层理论的视角,分析了在不同条件组合的情形下,地方政府创新实践可持续案例的特点。研究发现,中国地方政府存在三种主要的创新模式及条件组合：一是试点型创新,对应条件组合为创新扩散、学术关注和上级推广;二是地方型创新,对应条件组合为未创新扩散、未学术关注和未技术创新;三是技术型创新,对应条件组合为创新扩散、学术关注和技术创新。     

小微企业网贷风险定价模型研究

对小微企业网贷平台的运营模式进行深入分析,发现其运行机制包括需求的同质化解构、需求的模块化生产、需求的风险评估、个性化需求加总,在此基础上构建小微企业网贷风险定价模型。为验证模型的有效性,抽取国内典型互联网金融平台“人人贷”上近期发生的小微企业标的并进行风险定价,将定价结果与实际利率进行比较,发现该模型适用于“卖方市场”的小微企业网贷,能够提供差异化的贷款利率,从而向小微企业提供个性化的网贷产品。