Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field

Quality & Quantity - Machine learning algorithms and artificial neural networks promise a new and powerful approach for making better and more transferable predictions in global conflict...     

Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. To competitively exploit these products, one option is to incorporate used components in “new” or remanufactured products. However, this option is partly limited by a firm's ability to assess the reliability of used components. A comprehensive two-step approach is proposed. The first stage phase statistically analyzes the behavior of components for reuse. A well-known reliability assessment method, the Weibull analysis, is applied to the time-to-failure data to assess the mean life of components. In the second phase, the degradation and condition monitoring data are analyzed by developing an artificial neural network (ANN) model. The advantages of this approach over traditional approaches employing multiple regression analysis are highlighted with empirical data from a consumer product. Finally, the Weibull analysis and the ANN model are then integrated to assess the remaining useful life of components for reuse. This is a critical advance in sustainable management of supply chains since it allows for a better understanding of not only service requirements of product, but the remaining life in a product and hence its suitability for reuse or remanufacture. Future work should assess: (1) reduction in downtime of process equipment through the implementation of this technique as a means to better manage preventative maintenance; (2) reduce field failure of remanufactured product; (3) selling-service strategy through implementation of the proposed methodology.     

Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis

Many studies have applied backpropagation feedforward neural networks (BPNNs) as an alternative to multivariate discriminant analysis (MDA) in attempts to predict business distress using relatively small data sets. Although these studies have generally reported the superiority of BPNNs vs. MDA, they seem to ignore the fact that the former suffers from overfitting if the data set is too small compared to the free parameters of the network. We thus suggest an alternative approach that involves use of a probabilistic neural network (PNN). From our study of financially distressed Chinese public companies, we found that both the PNN and MDA algorithms provide good classifications. Relative to MDA, however, the PNN method provides better prediction, and, at the same time, does not require multivariate normality of the data. Our results appear to offer an improvement from those of earlier efforts that employ MDA, BPNN, and other models. In particular, PNN was here able to predict company distress with greater than 87.5% short-term accuracy, and 81.3% medium-term accuracy.     

Analysis of the probability of deletion of S&P 500 companies: Survival analysis and neural networks approach

We examine the probability of deletion of a firm from the S&P 500 index due to a decision of the index committee because the firm did not satisfy the index committee criteria. We study the probability of deletion with survival analysis and neural networks methods. We document that deletion might be predictable, which is contrary to the findings of most studies that the market cannot predict the timing of a company deletion from the S&P 500 index. It might also be beneficial to know ahead of time which company might be deleted from an index, to supplement the arbitrage opportunities that exist already in the announcement-effective date event window.     

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks

Likelihoods and posteriors of instrumental variable (IV) regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating posterior probabilities and marginal densities using Monte Carlo integration methods like importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm and the quality of the results greatly depend on the choice of the importance or candidate density. Such a density has to be ‘close’ to the target density in order to yield accurate results with numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from. A key step in the proposed class of methods is the construction of a neural network that approximates the target density. The methods are tested on a set of illustrative IV regression models. The results indicate the possible usefulness of the neural network approach.     

Classification performance of mathematical programming techniques in discriminant analysis: Results for small and medium sample sizes

The performance on small and medium-size samples of several techniques to solve the classification problem in discriminant analysis is investigated. The techniques considered are two widely used parametric statistical techniques (Fisher's linear discriminant function and Smith's quadratic function), and a class of recently proposed nonparametric estimation techniques based on mathematical programming (linear and mixed-integer programming). A simulation study is performed, analyzing the relative performance of the above techniques in the two-group case, for various small sample sizes, moderate group overlap and across six different data conditions. Training samples as well as validation samples are used to assess the classificatory performance of the techniques. The degree of group overlap and sample sizes selected for analysis in this paper are of interest in practice because they closely reflect conditions of many real data sets. The results of the experiment show that Smith's nonlinear quadratic function tends to be superior on the training samples and validation samples when the variances–covariances across groups are heterogeneous, while the mixed-integer technique performs best on the training samples when the variances–covariances are equal, and on validation samples with equal variances and discrete uniform independent variables. The mixed-integer technique and the quadratic discriminant function are also found to be more sensitive than the other techniques to the sample size, giving disproportionally inaccurate results on small samples.     

Gender differences in positive perceptions,anxiety, and depression among mothers and fathers of children with intellectual disabilities: a logistic regression analysis

This study explores gender differences in positive perceptions, anxiety, and depression among mothers and fathers of children with intellectual disabilities (IDs). We examined the relationship between these variables and certain characteristics of both the child (age and severity of disability) and the parents (age, educational level, and employment status). A sample of 60 mother/father couples who had children with IDs completed the Positive Contributions Scale to measure their positive perceptions, and the Hospital Anxiety and Depression Scale to assess their level of anxiety and depression. Bivariate analyses were used to determine differences between fathers and mothers as regards their positive perceptions and levels of anxiety and depression. A logistic regression model was then applied to identify which of the variables might be significant predictors of the gender differences observed among parents. Both mothers and fathers had positive perceptions of their children with IDs that co-existed with symptoms of anxiety and depression, with scores being higher among mothers. The predictive analysis of gender showed that individual variables (such as the employment status of both parents) may explain these differences.     

Asymptotic results and tests for the choice of approximative models in nonlinear two-phases regression models, heteroscedatic case

This paper is devoted to the study of the least squares estimator of f for the classical, fixed design, nonlinear model X (t _i)=f(t _i)+ε(t _i), i=1,2,…,n, where the (ε(t _i))_i=1,…,n are independent second order r.v.. The estimation of f is based upon a given parametric form. In Brodeau (1993) this subject has been studied in the homoscedastic case. This time we assume that the ε(t _i) have non constant and unknown variances σ²(t _i). Our main goal is to develop two statistical tests, one for testing that f belongs to a given class of functions possibly discontinuous in their first derivative, and another for comparing two such classes. The fundamental tool is an approximation of the elements of these classes by more regular functions, which leads to asymptotic properties of estimators based on the least squares estimator of the unknown parameters. We point out that Neubauer and Zwanzig (1995) have obtained interesting results for connected subjects by using the same technique of approximation. Received: February 1996     

Priors,posteriors and bayes factors for a Bayesian analysis of cointegration

Cointegration occurs when the long-run multiplier matrix of a vector autoregressive model exhibits rank reduction. Using a singular value decomposition of the unrestricted long-run multiplier matrix, we construct a parameter that reflects the presence of rank reduction. Priors and posteriors of the parameters of the cointegration model follow from conditional priors and posteriors of the unrestricted long-run multiplier matrix given that the parameter that reflects rank reduction is equal to zero. This idea leads to a complete Bayesian framework for cointegration analysis. It includes prior specification, simulation schemes for obtaining posterior distributions and determination of the cointegration rank via Bayes factors. We apply the proposed Bayesian cointegration analysis to the Danish data of Johansen and Juselius (Oxford Bull. Econom. Stat. 52 (1990) 169).     

Decreasing average cost in private schools,existence of majority voting equilibrium,and a policy analysis for Turkey

The existing models of mixed public–private school systems usually capture only the decreasing average cost faced by public schools, whereas empirical studies find evidence of it for private schools as well. Motivated by this, an equilibrium model of a mixed public–private school system is studied in this paper, whereby private schools also face decreasing average cost over enrollment. In the model, households, heterogeneous with respect to exogenously specified income and child’s ability, choose among a public and a private school. Private school charges tuition whereas public school is free. Public school spending is financed by income tax revenue collected from all households and the tax rate is determined via majority voting. Achievement of a child depends on its ability and education spending. Under the assumptions on the parameters of the model, a joint lognormal distribution of income and ability, and a Cobb–Douglas utility, majority voting equilibrium is numerically shown to exist. The model is calibrated to match certain statistics from the 2013 Turkish data. Using the calibrated model, we compare the benchmark for a mixed public–private school system with a pure public school system to understand the impact of shutting down some of the private schools in Turkey following the July 15 coup attempt. We find that mean achievement and variance of achievement after high school is \(0.039\%\) higher and \(0.013\%\) lower respectively in a pure public school system.     

Patent-based investment funds as innovation intermediaries for SMEs: In-depth analysis of reciprocal interactions,motives and fallacies

SMEs and academic spin-offs play an important role in generating innovations but face resource and competency constraints that have implications for commercializing their technologies. In two in-depth case studies we analyze how patent-based investment funds (PBIFs), operating as innovation intermediaries, help overcome these constraints. In contrast to other patent intermediaries, PBIFs acquire patents and patentable inventions at an early stage of technology development when access to capital is particularly critical. They invest in the technological and legal advancement of their portfolio, which is mostly conducted in close interaction with the technology-providing SMEs and their original inventors. These PBIFs not only provide high added-value in the course of technology commercialization but also operate international networks to facilitate the commercialization of SMEs' technologies at an international scale. Although PBIFs are a promising new agent in the heterogeneous field of innovation intermediaries, their commercialization results and the actual rate of returns are not yet well established.     

National science foundation experience with university-industry centers for scientific research and technological innovation (an analysis of issues, characteristics and criteria for their establishment)

Indications are that industry underinvests in innovation in terms of the ultimate benefits to the firm and society. Using N.S.F, experience as a basis, this article examines some of the issues that arise when government, the university, and industry interact in a cooperative cost sharing effort for stimulating technological innovation, as well as examining criteria and characteristics for establishing those university-industry partnerships that are most likely to succeed.     

改善优化营商环境的举措、成效与展望——基于世界银行《营商环境报告2020》的分析

市场经济条件下,处理好政府与市场关系、实现政府职能转变,是推进政府治理现代化需要解决的重要问题。近年来,我国十分注重通过优化营商环境推进政府职能转变,优化营商环境建设取得了一些积极成效,营商环境指数国际排名大幅提升,建设项目许可、保护少数投资者等指标大幅改善。依据世界银行《营商环境报告2020》的评价结果,下一步,应强化税务便利化,优化信贷环境,健全破产制度;促进营商环境向法治化、便利化、公平化方向发展,打造国际一流营商环境。     

基于资源基础理论的城市战略联盟动因分析——以厦漳泉城市战略联盟为例

城市之间基于资源的竞争合作与企业间具有相似之处。作为城市合作的重要途径,城市战略联盟是受到企业战略联盟成功经验的影响而产生的。通过对厦漳泉城市战略联盟的实证分析,从自然资源、环境资源、产业和企业资源,以及技术和人力资源的异质性与互补性等角度,解释了城市战略联盟产生与发展的具体动因。