企业财务预警的方法探讨

文章在借鉴宏观经济预警方法的基础上 ,探讨了几种企业财务危机预警的具体方法 ,这些方法具有较强的可操作性 。

10kV城市配电网可靠性基础参数预测方法分析

介绍了灰色模型预测法和非线性回归模型预测法的预测模型,分析了这两种预测方法的特点,在此基础上提出应根据历史数据的特点合理选择各种配电网可靠性基础参数的预测手段。以某地区配电网设备的故障率和预安排停电率为算例,阐述了各种预测方法的具体运用。算例预测结果表明:在样本容量小的情况下,当历史数据较平滑时灰色模型预测法有较大的优势;在存在不准确历史数据的情况下,采用非线性回归模型预测法更加准确。

NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor

In this study, Saccharomyces cerevisiae (baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine, which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process, and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control, experimental results demonstrate the robustness, effectiveness and advantages of the new controller.

Quantile Trends in Temperature Extremes in China

A number of recent studies have examined trends in extreme temperature indices using a linear regression model based on ordinary least-squares. In this study, quantile regression was, for the first time, applied to examine the trends not only in the mean but also in all parts of the distribution of several extreme temperature indices in China for the period 1960–2008. For China as a whole, the slopes in almost all the quantiles of the distribution showed a notable increase in the numbers of warm days and warm nights, and a significant decrease in the number of cool nights. These changes became much faster as the quantile increased. However, although the number of cool days exhibited a significant decrease in the mean trend estimated by classical linear regression, there was no obvious trend in the upper and lower quantiles. This finding suggests that examining the trends in different parts of the distribution of the time-series is of great importance. The spatial distribution of the trend in the 90 th quantile indicated that there was a pronounced increase in the numbers of warm days and warm nights, and a decrease in the number of cool nights for most of China, but especially in the northern and western parts of China, while there was no significant change for the number of cool days at almost all the stations.

Bayesian Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm

Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation ofpiecewise linear regression models. The method used to estimate the parameters ofpicewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC （Marcov Chain Monte Carlo） algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters ofpicewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm

In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. This paper proposes the Particle Swarm Optimization (PSO) algorithm for estimating the parameters such a curve. The PSO algorithm is an evolutional method based on a very simple concept. Comparison of PSO results with those of GA and LS methods showed that the PSO algorithm is more effective for estimating the parameters of the above curve.

资本结构对上市银行风险影响的实证研究——以我国16家A股上市银行为例

本文通过回顾2008年金融危机和《巴塞尔协议》的变动，强调了银行资本结构对抵御银行风险的重要作用。在理论研究上，详细分析了融资结构、股权结构和债务结构这三种具体形式的资本结构影响商业银行风险的作用机理，并以我国16家A股上市银行2010~2014年的财务数据为研究样本，对我国上市银行资本结构与风险的关系进行了实证研究，得出资本充足率、流通股比例、股本比例、一般风险准备比例和未分配利润比例均与风险成反比，股权集中度与风险成正比的结论，且影响程度有较大区别。最后，针对该结论提出了防范商业银行风险的几点建议。

Updating Methods for Real Time Flood Forecasting： A Comparison through Senegal River Basin Upstream Bakel

Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.

Analysis of Factors Determining the Demand for Water： A Case Study of Sabie Administrative Post, Moamba District, Mozambique

Water is an important resource for human being. However, it has been increasingly becoming the limited resource. Therefore, the debate of water issues has been centered in mechanisms to implement sustainable water management. Hence, understanding the determinants of water demand might help design appropriate water management policies, however, they are not known in Mozambique. Due to the lack of knowledge about the determinants of water demand in Mozambique in general and in Sabi6 in particular, the present study was conducted to analyse the factors determining the water demand for irrigation and domestic use using a linear regression model and travel cost method, respectively. The results show that an increase in 1 h of irrigation time increases the quantity demanded of irrigation water by 362.04 m3 and an increase in one irrigation per week increases the quantity demanded of irrigation water by 1,065.61 m3. Additionally, the results show that an increase in 1% of time spent in water collection decreases the number of trips by 0.3% and an increase in 1% in the number of household members involved in water collection decreases the number of the water collection trips by 0.23%. Household having private taps have less number of trips collecting water compared to those collecting water from public taps and boreholes as well as rivers. Therefore, the water demand for irrigation is determined by time spent for irrigation per day and the number of irrigations per week, and water demand for domestic use is determined by time spent for water collection, number of household members involved in water collection, the volumes of the containers used to collect water, the number of containers used to collect water, the quantity of water consumed by the household as well as the main source of water （river, boreholes and public tap）.

A learning method for energy optimization of the plug-in hybrid electric bus

The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.

Statistical Diagnosis and Gross Error Test for Semiparametric Linear Model

This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model.Several diagnostic measures and the methods for gross error testing are derived.Especially,the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail;at the same time,the paper proves that the data point deletion model is equivalent to the mean shift model for the semiparametric regression model.Finally,with one simulative computing example,some helpful conclusions are drawn.

Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models

The quantile estimation methods are proposed for functional-coefficient partially linear regression （FCPLR） model by combining nonparametric and functional-coefficient regression （FCR） model. The local linear scheme and the integrated method are used to obtain Focal quantile estimators of all unknown functions in the FCPLR model. These resulting estimators are asymptotically normal, but each of them has big variance. To reduce variances of these quantile estimators, the one-step backfitting technique is used to obtain the efficient quantile estimators of all unknown functions, and their asymptotic normalities are derived. Two simulated examples are carried out to illustrate the proposed estimation methodology.