指标可取负值的基于输入与输出的DEA模型

有关基于输入与输出的DEA模型,本文与现有文献的不同之处,一是模型中的评价指标可取负值,二是被评的决策单元可以不是所给的n个决策单元之一,三是模型并非由多目标规划模型推得.此外,给出了有关此模型的三个定理.因此,可知有关此模型的最优解存在的充分条件;在求解此模型后就能在判断决策单元的DEA有效性的同时计算出其相对效率,并能计算出其在DEA相对有效面上的"投影".     

具有两食饵趋向的食物链模型古典解的全局存在性

本文探讨在留曼边界条件下带有两食饵趋向和功能Ⅱ反应函数的三物种食物链模型,此模型的主要特征是捕食者捕食速度空间上的临时变化是由食饵的梯度决定的.应用压缩原理,抛物方程的Schauder估计和Lp估计,证明了此模型古典解的全局存在性.     

干旱区具脉冲灌溉的绿洲植被退化动力学模型分析 [英文]

本文研究干旱区具脉冲灌溉的绿洲植被退化动力学模型. 证明此模型的所有解一致完全有界. 得到此模型的绿洲植被灭绝周期解的全局渐近稳定的条件.也得到此模型持久的条件. 结论是脉冲灌溉方法为干旱区雨量减少控制绿洲植被退化提供可靠的策略基础.     

干旱区具脉冲灌溉的绿洲植被退化动力学模型分析(英文)

本文研究干旱区具脉冲灌溉的绿洲植被退化动力学模型.证明此模型的所有解一致完全有界.得到此模型的绿洲植被灭绝周期解的全局渐近稳定的条件.也得到此模型持久的条件.结论是脉冲灌溉方法为干旱区雨量减少控制绿洲植被退化提供可靠的策略基础.     

一个新的量子力学模型

讨论了具有一个Bose自由度的量子力学模型.用传统的方法构造量子力学模型时,需要知道相应的经典模型,并利用经典Poisson括号与正则量子对易子之间的对应关系来得到正则量子化条件.在讨论的量子模型里,不需要首先讨论相应的经典模型.此模型里,Lagrange量具有算子规范不变性,定域的理论中需引入相应的规范势.动力学变量的Euler-Lagrange运动方程就是通常的运动方程,而规范势仅仅给出一个约束,而此约束刚好是正则量子化条件.     

索赔次数为复合Poisson-Geometric过程的风险模型及破产概率

本文引入一类复合Poisson-Geometric分布,这类分布包括两个参数,是普通Poisson分布的一种推广,并在保险中有其实际的应用背景;基于此分布产生一个计数过程,称之为复合Poisson-Geometric过程.本文着重研究了索赔次数为复合Poisson-Geometric过程的风险模型,这种模型是经典风险模型的一个推广.针对此模型,本文给出了破产概率公式及更新方程.作为特例,当索赔额服从指数分布时,给出了破产概率的显式表达式.     

基于图方法的自回归和双线性时间序列模型系数的检验

自回归和双线性时间序列模型被表示为时间序列链图模型.在此基础上, 证明了自回归和双线性模型的系数为其他时间序列分量给定的条件下的条件相关系数.然后提出基于图的检验方法来检验自回归和双线性模型系数的显著性, 模拟结果表明此方法在水平和功效方面表现很好.     

政府开支模型中的收入分配与增长(英文)

本文建立了一个更一般的模型 ,在此模型中将政府开支分为消费型与生产型的 .并同时进入效用函数和生产函数 ,通过分析此模型可以得到政府开支外部性与增长速度成反比 ,而税收与增长速度的关系不明确 .     

约束下线性混合模型参数估计的渐近解及其收敛性

在线性不等式约束下讨论了具有相同参数的两个线性混合模型的参数估计问题,给出了一种迭代算法,得到了这类模型中参数的最小二乘估计序列及其渐近解.在此基础上,利用多元多项式方程组解的个数定理和不动点定理,证明了此估计序列是依概率1收敛的.     

基于BP模型的优化方法研究及其应用

首先根据BP算法的基本原理,推导出了BP模型中输出对输入的偏导.然后基于已有数据,利用BP模型进行拟合,保留训练后的BP模型的权值和阈值.在此基础上,利用变步长梯度法实现了BP模型的最优化.本文是对BP模型优化方法的一种新的理论探讨.     

MODELING INDIVIDUAL TREE MORTALITY RATES USING MARGINAL AND RANDOM EFFECTS REGRESSION MODELS

Abstract Developing models to predict tree mortality using data from long‐term repeated measurement data sets can be difficult and challenging due to the nature of mortality as well as the effects of dependence on observations. Marginal (population‐averaged) generalized estimating equations (GEE) and random effects (subject‐specific) models offer two possible ways to overcome these effects. For this study, standard logistic, marginal logistic based on the GEE approach, and random logistic regression models were fitted and compared. In addition, four model evaluation statistics were calculated by means of K‐fold cross‐valuation. They include the mean prediction error, the mean absolute prediction error, the variance of prediction error, and the mean square error. Results from this study suggest that the random effects model produced the smallest evaluation statistics among the three models. Although marginal logistic regression accommodated for correlations between observations, it did not provide noticeable improvements of model performance compared to the standard logistic regression model that assumed impendence. This study indicates that the random effects model was able to increase the overall accuracy of mortality modeling. Moreover, it was able to ascertain correlation derived from the hierarchal data structure as well as serial correlation generated through repeated measurements.     

Controlling predator-prey discrete dynamics utilizing a threshold policy with hysteresis

This paper introduces a threshold policy with hysteresis (TPH) for the control of the logistic one-species model, the Lotka-Volterra and Rosenzweig-MacArthur two species density-dependent predator-prey models. A nonstandard scheme is used for the discretization of the models since it results in preservation of the qualitative characteristics of the continuous-time models. Two theorems that establish the global stability of the discrete logistic model subject to the threshold policy (TP) and the TPH are proved. The proposed policy (TPH) is more realistic than a pure threshold policy (TP) proposed earlier in the literature and changes the dynamics of the system in such a way that a low amplitude bounded oscillation, far from the extinction region, is achieved. Furthermore, it can be designed by a suitable choice of so called virtual equilibrium points in a simple and intuitive manner.     

On the impact of model selection on predictor identification and parameter inference

We assessed the ability of several penalized regression methods for linear and logistic models to identify outcome-associated predictors and the impact of predictor selection on parameter inference for practical sample sizes. We studied effect estimates obtained directly from penalized methods (Algorithm 1), or by refitting selected predictors with standard regression (Algorithm 2). For linear models, penalized linear regression, elastic net, smoothly clipped absolute deviation (SCAD), least angle regression and LASSO had a low false negative (FN) predictor selection rates but false positive (FP) rates above 20 % for all sample and effect sizes. Partial least squares regression had few FPs but many FNs. Only relaxo had low FP and FN rates. For logistic models, LASSO and penalized logistic regression had many FPs and few FNs for all sample and effect sizes. SCAD and adaptive logistic regression had low or moderate FP rates but many FNs. 95 % confidence interval coverage of predictors with null effects was approximately 100 % for Algorithm 1 for all methods, and 95 % for Algorithm 2 for large sample and effect sizes. Coverage was low only for penalized partial least squares (linear regression). For outcome-associated predictors, coverage was close to 95 % for Algorithm 2 for large sample and effect sizes for all methods except penalized partial least squares and penalized logistic regression. Coverage was sub-nominal for Algorithm 1. In conclusion, many methods performed comparably, and while Algorithm 2 is preferred to Algorithm 1 for estimation, it yields valid inference only for large effect and sample sizes.     

USING RESERVES TO PROTECT FISH AND WILDLIFE SIMPLIFIED MODELING APPROACHES

ABSTRACT. This paper investigates theoretically to what extent a nature reserve may protect a uniformly distributed population of fish or wildlife against negative effects of harvesting. Two objectives of this protection are considered: avoidance of population extinction and maintenance of population, at or above a given precautionary population level. The pre‐reserve population is assumed to follow the logistic growth law and two models for post‐reserve population dynamics are formulated and discussed. For Model A by assumption the logistic growth law with a common carrying capacity is valid also for the post‐reserve population growth. In Model B, it is assumed that each sub‐population has its own carrying capacity proportionate to its distribution area. For both models, migration from the high‐density area to the low‐density area is proportional to the density difference. For both models there are two possible outcomes, either a unique globally stable equilibrium, or extinction. The latter may occur when the exploitation effort is above a threshold that is derived explicitly for both models. However, when the migration rate is less than the growth rate both models imply that the reserve can be chosen so that extinction cannot occur. For the opposite case, when migration is large compared to natural growth, a reserve as the only management tool cannot assure survival of the population, but the specific way it increases critical effort is discussed.     

Quadratic interval innovation diffusion models for new product sales forecasting

An appropriate sales forecasting method is vital to the success of a business firm. The logistic model and the Gompertz model are usually adopted to forecast the growth trends and the potential market volume of innovative products. All of these models rely on statistics to explain the relationships between dependent and independent variables, and use crisp parameters. However, fuzzy relationships are more appropriate for describing the relationships between dependent and independent variables; these relationships require less data than traditional models to generate reasonable estimates of parameters. Therefore, we have combined fuzzy regression with the logistic and Gompertz models to develop a quadratic-interval Gompertz model and a quadratic-interval logistic model, and we applied the models to three cases. Our practical application of the two models shows that they are appropriate tools that can reveal the best and worst possible sales volume outcomes.     

Estimating bank default with generalised extreme value regression models

The paper proposes a novel model for the prediction of bank failures, on the basis of both macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the paper we apply a regression method for binary data based on extreme value theory, which turns out to be more effective than classical logistic regression models, as it better leverages the information in the tail of the default distribution. The application of this model to the occurrence of bank defaults in a highly bank dependent economy (Italy) shows that, while microeconomic factors as well as regulatory capital are significant to explain proper failures, macroeconomic factors are relevant only when failures are defined not only in terms of actual defaults but also in terms of mergers and acquisitions. In terms of predictive accuracy, the model based on extreme value theory outperforms classical logistic regression models.     

A semigroup approach to age-dependent population dynamics with time delay

We study the McKendrick type models of population dynamics with instantaneous time delay in the birth rate. The models involve first order partial differential equations with nonlocal and delayed boundary conditions. We show that a semigroup can be associated

to it and identify the infinistimal generator. Its spectral properties are analyzed yielding large time behaviour. An interesting result is that if the total population converges to an equilibrium it will converge to it in an oscillatory fashion. Further, we consider a logistic ara age-dependent model with delay. A nonlinear semigroup is constructed to describe the evolution of the population. Existence and uniqueness of the nonlinear equation are proved.     

A revenue management approach to address a truck rental problem

This paper describes an application of revenue management techniques and policies in the field of logistics and distribution. In particular, the problem of transportation operators, who offer products for hire, is considered. A product is a truck of a given capacity, which can be rented for one or several time periods, throughout a multi-period planning horizon. The logistic operator can satisfy the demand of a given product with trucks with a capacity greater than that initially required, that is an ‘upgrade’ can take place. In this context, the logistic operator has to decide whether to accept or reject a request and which type of truck should be used to address it. For this purpose, a dynamic programming (DP) formulation of the problem under consideration is devised. The ‘course of dimensionality’ leads to the necessity of introducing different mathematical programming models to represent the problem. The mathematical models we presented are an extension of the well-known approximations for the DP of traditional network capacity management analysis. Based on these models and exploiting revenue management concepts, primal and dual acceptance policies are developed and compared in a computational study.     

Analysis of two-sample truncated data using generalized logistic models

Parallel to Cox's [JRSS B34 (1972) 187-230] proportional hazards model, generalized logistic models have been discussed by Anderson [Bull. Int. Statist. Inst. 48 (1979) 35-53] and others. The essential assumption is that the two densities ratio has a known parametric form. A nice property of this model is that it naturally relates to the logistic regression model for categorical data. In astronomic, demographic, epidemiological, and other studies the variable of interest is often truncated by an associated variable. This paper studies generalized logistic models for the two-sample truncated data problem, where the two lifetime densities ratio is assumed to have the form exp{α+φ(x;β)}. Here φ is a known function of x and β, and the baseline density is unspecified. We develop a semiparametric maximum likelihood method for the case where the two samples have a common truncation distribution. It is shown that inferences for β do not depend the nonparametric components. We also derive an iterative algorithm to maximize the semiparametric likelihood for the general case where different truncation distributions are allowed. We further discuss how to check goodness of fit of the generalized logistic model. The developed methods are illustrated and evaluated using both simulated and real data.