MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms

We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.     

Bayesian Variable Selection via Particle Stochastic Search

We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), which we refer to as particle stochastic search (PSS). We illustrate PSS through applications to linear regression and probit models.     

On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction

We consider a proteomic mass spectrometry case-control study for the calibration of a diagnostic rule for the detection of early-stage breast cancer. For each patient, a pair of two distinct mass spectra is recorded, each of which is derived from a different prior fractionation procedure on the available patient serum. We propose a procedure for combining the distinct spectral expressions from patients for the calibration of a diagnostic discriminant rule. This is achieved by first calibrating two distinct prediction rules separately, each on only one of the two available spectral data sources. A double cross-validatory approach is used to summarize the available spectral data using the two classifiers to posterior class probabilities, on which a combined predictor can be calibrated.     

中国区域就业结构与区域收入差异的实证研究—基于panel-data模型

运用面板数据模型(Panel-data Model)分析了直辖市、东中西部三大产业的就业结构对区域收入差异的影响,不同区域的就业结构对区域的收入差异影响不同,针对不同区域情况,提出了协调发展产业,调整产业就业结构.促使区域差异缩小的政策建议.     

随机利率下服从指数O-U过程的可转换债券的鞅定价

从定量的角度分析了随机利率下有股利分配的可转换债券的价值构成,并在股价服从广义O-U过程的条件下,利用鞅定价方法推导出可转换债券的定价公式.     

Robust equilibrium excess-of-loss reinsurance and CDS investment strategies for a mean–variance insurer with ambiguity aversion

This paper considers the robust equilibrium reinsurance and investment strategies for an ambiguity-averse insurer under a dynamic mean–variance criterion. The insurer is allowed to purchase excess-of-loss reinsurance and invest in a financial market consisting of a risk-free asset and a credit default swap (CDS). Following a game theoretic approach, robust equilibrium strategies and equilibrium value functions for the pre-default case and the post-default case are derived, respectively. For the ambiguity-averse insurer, in general the equilibrium strategies can be characterized by unique solutions to some algebraic equations. For the degenerate case with an ambiguity-neutral insurer, closed-form expressions of equilibrium strategies and equilibrium value functions are obtained. Numerical examples demonstrate that the consideration of model uncertainty and CDS investment improves the insurer’s utility. In this regard, our paper establishes theoretical and numerical support for the importance of ambiguity aversion, credit risk and their interplay in insurance business.     

Affordable and adequate annuities with stable payouts: Fantasy or reality?

This paper introduces a class of unit-linked annuities that extends existing annuities by allowing portfolio shocks to be gradually absorbed into the annuity payouts. Consequently, our new class enables insurers to offer an affordable and adequate annuity with a stable payout stream. We show how to price and adequately hedge the annuity payouts in a general financial environment. In particular, our model accounts for various stylized facts of stock returns such as asymmetry and heavy-tailedness. Furthermore, the generality of our framework makes it possible to explore the impact of a parameter misspecification on the annuity price and the hedging performance.     

Modelling silicosis: Existence,uniqueness and basic properties of solutions

We present a model for the silicosis disease mechanism following the original proposal by Tran et al. (1995), as modified recently by da Costa et al. (2020). The model consists in an infinite ordinary differential equation system of coagulation–fragmentation–death type. Results of existence, uniqueness, continuous dependence on the initial data and differentiability of solutions are proved for the initial value problem.     

融合学习模型的岩石光谱特征自动分类

岩石光谱综合反映了岩石的物理化学性质、成分及其结构构造。岩石光谱数据已被应用于岩石分类的研究,但是不同于矿物光谱,岩石光谱并无标准数据库,且受较多干扰因素影响,例如矿物组分、结构构造、化学成分、风化力度,测量仪器的误差等。传统岩石光谱分类模型先是对岩石光谱进行预处理排除干扰,然后采用不同方法对部分光谱特征分析,以达到分类目的。但对光谱数据特征遗失较多,使得分类准确率低下且操作过程繁琐、效率不高。因此,建立一个简单、快速、准确的岩石光谱自动分类模型具有重要意义。机器学习能够对获得的所有数据进行学习,不存在遗漏,大大提高了分类精度,且是对原始数据直接操作,不需预处理,简化流程。为此,选取辽宁兴城地区作为研究区,采集了若干种典型岩石样本,利用美国ASD便携式光谱仪实测光谱,最终获得608条数据,依据岩石光谱特征分为三类进行研究。首先利用决策树(DT)及决策树的升级模型--随机森林（RF）对数据进行分类,但当数据噪音较大时随机森林容易陷入过拟合;因而利用对异常值不敏感的K-最近邻（KNN）建模,但KNN需要对每个样本都考虑,数据量大时计算量会很大,效率不高;所以通过支持向量机（SVM）来提升分类准确率。从实验结果可以看出,4种分类模型的准确率排序为：SVM>KNN>RF>DT。为进一步提高岩石光谱特征的自动分类精度,采取了融合多个不同模型的办法,即对不同模型的分类结果进行投票,选择投票最多的作为最后分类结果。由于硬投票可在一定程度上减少过拟合现象的发生,更加适合分类模型,所以利用硬投票法融合了RF、KNN与SVM三个机器学习模型,最终的分类准确率可达到99.17%。综上所述,基于融合学习模型进行岩石光谱特征自动分类是切实可行且准确高效的。     

提高近红外光谱法检测人体血液等复杂溶液成分准确度的研究进展

近红外光谱常被用于含氢有机物质等的物化参数测量,可以提供丰富的结构和组成信息在复杂溶液的光谱定量分析中更是被广泛应用。然而在人体血液等复杂溶液的近红外光谱分析中,强大的背景信息造成的噪声干扰和冗余变量的存在,严重影响着样品的光谱测量和分析,影响着分析的效率和准确度。如何消除背景噪声等的干扰来提高分析准确度已经引起高度重视,近几年来,国内外学者提出了许多基于化学计量学方法的相关方法。从光谱预处理、变量优化和建模分析三方面,以传统的化学计量学方法出发,总结和分析这些方法在人体血液等复杂溶液的近红外光谱定量分析的应用和各自的特点,为提高光谱定量分析准确度的研究提供参考。