Reconstruction of populations by stochastic optimization: Sensitivity analysis

The reconstruction of populations by stochastic optimization solves the nontrivial problem of finding demographic flows from population registers or vital statistics and censuses, if available. These flows allow the reconstruction of stocks (age pyramids and vital statistics). After a review of reconstruction methods, the sensitivity analysis shows the robustness of the method by stochastic optimization to flawed or missing values, to the length of the reconstruction period, and to variations in the actual demographic flows.     

基于潜类别随机前沿的区域创新效率及其影响因素分析

运用可根据研究对象的潜在属性内生分组的潜类别随机前沿模型,采用1999-2012年中国各省区数据,研究各省区的创新效率及影响因素。结果表明:以人力资本水平和基础设施状况为条件变量,将全国各省区分成两个技术类别,分别有各自的技术前沿和函数形式,A类别中上海市的创新效率最高,B类别中河北省的创新效率最高;平均来看,各类的创新效率均呈上升趋势,贸易开放、产业结构和金融发展对创新效率均有显著的正向作用,同时创新效率在各类内部均存在俱乐部收敛。     

Peter Schmidt: Econometrician and consummate professional

Peter Schmidt has been one of its best-known and most respected econometricians in the profession for four decades. He has brought his talents to many scholarly outlets and societies, and has played a foundational and constructive role in the development of the field of econometrics. Peter Schmidt has also served and led the development of Econometric Reviews since its inception in 1982. His judgment has always been fair, informed, clear, decisive, and constructive. Respect for ideas and scholarship of others, young and old, is second nature to him. This is the best of traits, and Peter serves as an uncommon example to us all. The seventeen articles that make up this Econometric Reviews Special Issue in Honor of Peter Schmidt represent the work of fifty of the very best econometricians in our profession. They honor Professor Schmidt's lifelong accomplishments by providing fundamental research work that reflects many of the broad research themes that have distinguished his long and productive career. These include time series econometrics, panel data econometrics, and stochastic frontier production analysis.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Modeling Correlated Discrete Uncertainties in Event Trees with Copulas

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.     

On tail behaviour of kth upper order statistics under fixed and random sample sizes via tail equivalence

For a fixed positive integer k, limit laws of linearly normalized kth upper order statistics are well known. In this article, a comprehensive study of tail behaviours of limit laws of normalized kth upper order statistics under fixed and random sample sizes is carried out using tail equivalence which leads to some interesting tail behaviours of the limit laws. These lead to definitive answers about their max domains of attraction. Stochastic ordering properties of the limit laws are also studied. The results obtained are not dependent on linear norming and apply to power norming as well and generalize some results already available in the literature. And the proofs given here are elementary.     

Stochastic correlation coefficient ensembles for variable selection

In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy.     

Mapping Invasive Species Risks with Stochastic Models: A Cross-Border United States-Canada Application for Sirex noctilio Fabricius

Nonindigenous species have caused significant impacts to North American forests despite past and present international phytosanitary efforts. Though broadly acknowledged, the risks of pest invasions are difficult to quantify as they involve interactions between many factors that operate across a range of spatial and temporal scales: the transmission of invading organisms via various pathways, their spread and establishment in new environments. Our study presents a stochastic simulation approach to quantify these risks and associated uncertainties through time in a unified fashion. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. We simulate new potential entries of S. noctilio as a stochastic process, based on recent volumes of marine shipments of commodities from countries where S. noctilio is established, as well as the broad dynamics of foreign marine imports. The results are then linked with a spatial model that simulates the spread of S. noctilio within the geographical distribution of its hosts (pines) while incorporating existing knowledge about its behavior in North American landscapes. Through replications, this approach yields a spatial representation of S. noctilio risks and uncertainties in a single integrated product. The approach should also be appealing to decisionmakers, since it accounts for projected flows of commodities that may serve as conduits for pest entry. Our 30-year forecasts indicate high establishment probability in Ontario, Quebec, and the northeastern United States, but further southward expansion of S. noctilio is uncertain, ultimately depending on the impact of recent international treatment standards for wood packing materials.     

Dependence function for continuous bivariate densities

The notion of cross-product ratio for discrete two-way contingency table is extended to the case of continuous bivariate densities. This results in the “local dependence function” that measues the margin-free dependence between bivariate random variables. Properties and examples of the dependence function are discussed. The bivariate normal density plays a special role since it has constant dependence. Continuous bivariate densities can be constructed by specifying the dependence function along with two marginals in analogy to the construction of two-way contingency tables given marginals and patterns of interaction. The dependence function provides a partial ordering on bivariate dependence.     

Bayesian inference of asymmetric stochastic conditional duration models

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes and innovations of observed duration process and latent log duration process. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allow comparison of our models and methods to those existing in the literature.