Reject inference,augmentation, and sample selection

Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Alternatively, practitioners often see the problem as one of missing data where the relationship in the new model is biased because the behaviour of the omitted cases differs from that of those who make up the sample for a new model. To attempt to correct for this, differential weights are applied to the new cases. The aim of this paper is to see if the use of both a Heckman style sample selection model and the use of sampling weights, together, will improve predictive performance compared with either technique used alone. This paper will use a sample of applicants in which virtually every applicant was accepted. This allows us to compare the actual performance of each model with the performance of models which are based only on accepted cases.     

基于免疫遗传算法的多重序列比对

提出一种基于免疫遗传算法的多重序列比对的方法，它将一种免疫算子加入到遗传算法的框架中，通过对个体接种疫苗来进一步提升个体的存活能力，实验结果表明，该方法可以加快收敛速度，并能求出比遗传算法更优的解。     

Comparative molecular field analysis and energy interaction studies of thrombin-inhibitor complexes

A Comparative Molecular Field Analysis (CoMFA) and an interaction energy-based method were applied on a database holding the 3D structures of 29 thrombin-inhibitor complexes. Several parameters were optimized in both methods in order to obtain the best correlation between theoretical and experimentally determined binding (Ki) data. CoMFA, which only uses the information of the inhibitors, performed best (r = 0.99, q2 = 0.46, N = 29) when HF 6-31G charges were used in combination with a pharmacophore-based alignment. Inclusion of hydrophobic fields did not lead to improvements. The interaction energy-based approach uses the information of the whole thrombin-inhibitor complex. A statistically significant correlation (r = 0.74, N = 14) could only be obtained for a subset of the database containing the high resolution structures. Geometry optimization of the ligand only in combination with downscaled electrostatics performed best.     

Scoring Functions – the First 100 Years

The use of simple linear mathematical models to estimate chemical properties is not a new idea. Albert Einstein used very simple ‘gravity-like' forces to explain the capillarity of different liquids in 1900–1901. Today such models are used in more complicated situations, and a great many have been developed to analyse interactions between proteins and their ligands. This is not surprising, since proteins are too complicated to model accurately without lengthy numerical analysis, and simple models often do at least as good a job in predicting binding constants as much more computationally expensive methods. One hundred years after Einstein’s ‘miraculous year’ in which he transformed physics, it is instructive to recall some of his even earlier work. As approximations, ‘scoring functions’ are excellent, but it is dangerous to read too much into them. A few cautionary tales are presented for the beginner to the field of ligand affinity prediction by linear models.     

重症胰腺炎的治疗策略

目的 :应用 APACHE 连续评分判断重症胰腺炎的严重性、预后和并发症 ,决定重症胰腺炎手术和再次手术时机。方法 :1991年 1月至 1998年 1月治疗重症胰腺炎 2 8例。急诊手术 2 3例 ,其中单次手术13例 ,多次手术 10例 ,非手术 5例。采用 APACHE 连续评分 ,间隔 2 4～ 48小时评分一次。死亡组与非死亡组连续评分比较 ,手术组与非手术组连续评分比较 ,单次手术组与多次手术组连续评分比较。结果 :连续评分逐日上升或居高不下是预后恶劣的重要信号 ,连续评分陡升说明发生并发症。结论 :连续 APACHE 评分对于判断重症胰腺炎的严重性和预后优于单次评分。单次评分不能预测并发症的发生 ,而连续评分可从其趋势上预知是否有重大并发症发生 ,并可据此决定手术适应证和再次手术适应证。     

Granting and managing loans for micro-entrepreneurs: New developments and practical experiences

We present a methodology to grant and follow-up credits for micro-entrepreneurs. This segment of grantees is very relevant for many economies, especially in developing countries, but shows a behavior different to that of classical consumers where established credit scoring systems exist. Parts of our methodology follow a proven procedure we have applied successfully in several credit scoring projects. Other parts, such as cut-off point construction and model follow-up, however, had to be developed and constitute original contributions of the present paper. The results from two credit scoring projects we developed in Chile, one for a private bank and one for a governmental credit granting institution, provide interesting insights into micro-entrepreneurs’ repayment behavior which could also be interesting for the respective segment in countries with similar characteristics.     

基于项目反应理论多级评分的大学英语考试评价模型

大学英语的教学效果涉及全国几百万学子的外语能力,其意义直接影响着我国经济和社会的发展.作为一种经典的能力测验评分手段,项目反应理论应用于大学英语教学效果的评价是一种新的尝试.本文利用项目反应理论,对大学英语考试构建了一个多级评分的考生能力评价模型,它可以作为单级评分的二参数logistic模型的推广.同时对于能力参数,我们给出了极大似然估计的迭代步骤.     

Performance evaluation of maximal separation techniques in immunohistochemical scoring of tissue images

This paper presents an automatic scoring method for p53 immunostained tissue images of oral cancer that consist of tissue image segmentation, splitting of clustered nuclei, feature extraction and classification. The tissue images are segmented using entropy thresholding technique in which the optimum threshold value to each color component is obtained by maximizing the global entropy of its gray-level co-occurrence matrix and clustered cells are separated by selectively applying marker-controlled watershed transform. Cell nuclei feature is extracted by maximal separation technique (MS) based on blue component of tissue image and subsequently, each cell is classified into one of four categories using multi-level thresholding. Finally, IHC score of tissue images have been determined using Allred method. A statistical analysis is performed between immuno-score of manual and automatic method, and compared with the scores that have obtained using other MS techniques. According to the performance evaluation, IHC score based on blue component that has high correlation coefficients (CC) of 0.95, low mean difference (MD) of 0.15, and a very close range of 95% confidence interval with manual scores. Therefore, automatic scoring method presented in this paper has high potential to help the pathologist in IHC scoring of tissue images.     

聚晶金刚石复合片非脆性去除磨削机理研究

聚晶金刚石(polycrystalline diamond,PCD)的磨削是PCD刀具制造中的关键环节,磨削参数的变化影响PCD的去除方式,进而决定刀具的切削性能.通过扫描电镜观察分析腐蚀后的PCD磨削表面微观形貌,研究了金刚石砂轮磨削PCD中的刻划作用、滑擦作用和热化学反应等非脆性去除机理,及其与磨削参数的关系.结果表明:刻划作用仅在砂轮磨粒较锋利时产生作用,多存在于湿磨初期;砂轮磨钝后,滑擦作用和热化学反应是PCD的主要去除方式,且滑擦作用与热化学反应同时发生.通过设计一组特殊磨削试验,间接证明了磨削后的PCD表面存在由热化学反应产生的软化层.     

Credit scoring for profitability objectives

In consumer credit markets lending decisions are usually represented as a set of classification problems. The objective is to predict the likelihood of customers ending up in one of a finite number of states, such as good/bad payer, responder/non-responder and transactor/non-transactor. Decision rules are then applied on the basis of the resulting model estimates. However, this represents a misspecification of the true objectives of commercial lenders, which are better described in terms of continuous financial measures such as bad debt, revenue and profit contribution. In this paper, an empirical study is undertaken to compare predictive models of continuous financial behaviour with binary models of customer default. The results show models of continuous financial behaviour to outperform classification approaches. They also demonstrate that scoring functions developed to specifically optimize profit contribution, using genetic algorithms, outperform scoring functions derived from optimizing more general functions such as sum of squared error.