Detection of jump location curve in spatial linear regression model with two-dimensional threshold

In the context of spatial linear regression, we discuss detection of jump location curve treated as threshold curve which cannot be expressed by independent variables but indirectly determines two specific model forms. The threshold curve in this paper is described by a straight line with two location variables, longitude and latitude, and can be estimated by maximizing the coefficient difference between two one-sided linear regression models. Theoretical results show that the estimator is consistent. Our method performs well by numerical studies.     

基于Logistic生长方程—马尔可夫模型的港口投资风险测度

伴随着我国经济从高速增长逐渐向经济新常态转变，港口的发展也从高速增长阶段逐渐趋近平稳，随之而来的港口风险问题日益突出。由于港口的发展同中国经济发展相似，呈现非常明显的非线性发展特征，这样采用传统的单一模型很难刻画这一现实问题。为此，本文首次构造了Logistic生长方程—马尔可夫组合模型，通过Logistic生长方程生成马尔可夫模型所需要的状态参数，在此基础上构造风险转移概率矩阵，有效的解决了港口投资风险的测度问题。本文选取1985至2014年的样本数据进行验证，结果表明：2016年后港口投资风险会突显出来，并将持续较长一段时期，表明我国港口正处在转型升级的关键时期，需加快从外延式扩张向内涵式发展转变，从粗放式到精细化管理转变。     

Predicting hospital census using time series regression methods

We first describe the time series modeling problem in a general way. Then some specific assumptions and observations which are pertinent to the application of these models are made. We next propose a specific approach to the modeling problem, one which yields efficient, easily calculated estimators of all parameters (under the stated assumptions). Finally, the technique is applied to the problem of modeling the census of a particular hospital.     

Ensemble Habitat Mapping of Invasive Plant Species

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species‐environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species‐environment matching models for risk analysis.     

Pairwise directions estimation for multivariate response regression data

This article concerns the analysis of multivariate response data with multi-dimensional covariates. Based on local linear smoothing techniques, we propose an iteratively adaptive estimation method to reduce the dimensions of response variables and covariates. Two weighted estimation strategies are incorporated in our approach to provide initial estimates. Our proposal is also extended to curve response data for a data-adaptive basis function searching. Instead of focusing on goodness of fit, we shift the problem to reveal the data structure and basis patterns. Simulation studies with multivariate response and curve data are conducted for our pairwise directions estimation (PDE) approach in comparison with sliced inverse regression of Li et al. [Dimension reduction for multivariate response data. J Amer Statist Assoc. 2003;98:99–109]. The results demonstrate that the proposed PDE method is useful for data with responses approximating linear or bending structures. Illustrative applications to two real datasets are also presented.     

Additive time-dependent hazard model with doubly truncated data

For doubly truncated data, i.e. the variables of interest are only observable if they lie in a certain random interval, an additive hazard model with time-dependent regression coefficients is investigated. Consistency and asymptotic normality are proven under mild assumptions. A simulation study investigates the finite sample properties and the influence of the truncation distribution on the estimation error. Finally, the method is applied to a doubly truncated data set of German companies, where the age at insolvency is of interest.     

Quantile regression methods for left-truncated and right-censored data

Left-truncated and right-censored (LTRC) data are encountered frequently due to a prevalent cohort sampling in follow-up studies. Because of the skewness of the distribution of survival time, quantile regression is a useful alternative to the Cox's proportional hazards model and the accelerated failure time model for survival analysis. In this paper, we apply the quantile regression model to LTRC data and develops an unbiased estimating equation for regression coefficients. The proposed estimation methods use the inverse probabilities of truncation and censoring weighting technique. The resulting estimator is uniformly consistent and asymptotically normal. The finite-sample performance of the proposed estimation methods is also evaluated using extensive simulation studies. Finally, analysis of real data is presented to illustrate our proposed estimation methods.     

中国的通货膨胀及其不确定性

采用1990年1月以来居民消费价格指数(CPI)的月度数据,运用随机域回归模型、系列随机域的非线性检验方法和贝叶斯估计方法,对中国通货膨胀率与通货膨胀不确定性的关系进行了实证分析。研究发现:通货膨胀率与通货膨胀之间具有双向关系。通货膨胀率引起了通货膨胀不确定性,两者呈现U型关系;较高的通货膨胀不确定性引起通货膨胀率先升后降,呈现倒U曲线关系。