养殖大菱鲆血细胞形态和相关生理指标的测定

研究了大菱鲆血细胞的类型、形态以及血液生理学指标.大菱鲆的血细胞可区分为红细胞、淋巴细胞、单核细胞、嗜中性粒细胞和血栓细胞5种类型,没有发现嗜酸性粒细胞和嗜碱性粒细胞.在白细胞中,血栓细胞和小淋巴细胞较小,嗜中性粒细胞和单核细胞较大.血细胞计数测得红细胞密度为(2.73±0.22)×106个/mm3,白细胞密度为(1.79±0.25)×104个/mm3.淋巴细胞、单核细胞、嗜中性粒细胞和血栓细胞在白细胞中所占的比例分别为:(24.72±6.26)%,(5.26±1.95)%,(13.45±3.31)%和(56.60±9.52)%.血红蛋白含量为(6.63±0.58)g/100mL,红细胞渗透脆性为(0.38±0.04)%NaCl,凝血时间为(80±12)s.     

科技力与区域金融综合竞争力的模糊曲线分析

针对区域金融研究现状的不足,利用复杂系统原理,建立了全面、客观的金融综合竞争力指标体系,将其分为显示性指标和解释性指标两部分,选取31个地区作为样本,运用主成分和因子分析法对各个区域的金融成长状况进行计量分析研究和排序,并在此基础上进行聚类分析;在解释性指标的模糊曲线分析中,选取科技力指标,通过分析得出该指标以及构成该指标的各个分力与金融综合竞争力存在显著相关关系。科技力对区域金融综合竞争力的解释力和贡献率很高,因此科技力量对金融的发展水平起着决定性作用。     

偏正态随机波动模型及其实证检验

首先构建了有杠杆效应的随机波动模型(SV-L),证明了其波动随机项的条件分布为两个偏正态分布,由此称该模型为偏正态随机波动模型(SV-SN).接下来讨论了SV-SN模型的经济含义以及对应随机波动项的统计特征.最后利用沪深两市的指数收益数据对模型进行了实证研究,其结论为:相对于一般的SV模型,SV-SN模型的拟合效果更好;新息具有减弱后期波动之效应;与理论预期一致,单位负新息比单位正新息引致的波动要大.     

Functions of Concordance and Dependence with Related Measures

Some functions that serve as building blocks for construction of a wider range of modes of concordance and dependence are pointed. We probe into interplays of such modes. From the standpoint of their conformity to stochastic dominance ordering of distributions within a Fréchet class, all such derived modes display some parallelism under certain conditions. We finally suggest a novel numeric measure of dependence that covers similar existing measures in literature.     

Proposition of a General Formula for Price Indices

In this article, we propose a general formula for aggregative price indices that satisfies most postulates coming from the axiomatic price index theory. We show that the ideal Fisher index, Laspeyres and Paasche formulas, and a lot of other indices are particular cases of the proposed formula. Moreover, using the general formula we can easily define new indices, that would satisfy given postulates. We also present an interesting result for the proposed formula.     

Measuring and Decomposing Productivity Change

Measuring productivity change with Malmquist indices has become common practice, because they are easily computed using nonparametric programming techniques and can be readily decomposed into technical and efficiency change. However, this approach is nonstochastic and requires a constant returns to scale assumption to construct the reference technology. We propose estimating productivity change using a stochastic input distance frontier, imposing no restrictions on returns to scale. We derive the analogous decomposition of productivity change and develop a generalized method of moments strategy in which outputs or inputs may be endogenous. We compare two methods in an application to electric utilities.     

Detecting Randomness: A Review of Existing Tests with New Comparisons

In this article we review and compare a number of existing tests for detecting randomness in time series data, with emphasis on stock market index data. By comparing variance ratio tests with traditional statistical tests, we have the most extensive simulation comparison of such procedures. The investigated tests are compared over a diverse group of distributions, models, and stock market applications. In our stock market data analysis, the choice of data transformation can have a noticeable effect on test results. This study provides the reader with a guide as to which test and transformation is most appropriate for their use.     

Accuracy Analysis of the Percentile Method for Estimating Non Normal Manufacturing Quality

We consider asymmetric kernel estimates based on grouped data. We propose an iterated scheme for constructing such an estimator and apply an iterated smoothed bootstrap approach for bandwidth selection. We compare our approach with competing methods in estimating actuarial loss models using both simulations and data studies. The simulation results show that with this new method, the estimated density from grouped data matches the true density more closely than with competing approaches.     

Estimation of Sobol's sensitivity indices under generalized linear models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify through the SSI analysis of a public microarray dataset several novel higher-order gene–gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package Sobol sensitivity.     

Modelling process robustness: a case study of centrifugal casting

A robust process minimises the effect of the noise factors on the performance of a product or process. The variation of the performance of a robust process can be measured through modelling and analysis of process robustness. In this paper, a comprehensive methodology for modelling and analysis of process robustness is developed considering a number of relevant tools and techniques such as multivariate regression, control charting and simulation within the broad framework of Taguchi method. The methodology as developed considers, in specific terms, process modelling using historical data pertaining to responses, inputs variables and parameters as well as simulated noise variables data, identification of the model responses at each experimental setting of the controllable variables, estimation of multivariate process capability indices and control of their variability using control charting for determining optimal settings of the process variables using design of experiment-based Taguchi Method. The methodology is applied to a centrifugal casting process that produces worm-wheels for steam power plants in view of its critical importance of maintaining consistent performance in various under controllable situations (input conditions). The results show that the process settings as determined ensure minimum in-control variability with maximum performance of the centrifugal casting process, indicating improved level of robustness.