优序融资与权衡理论比较研究--基于面板数据分数回归模型的实证

优序融资理论和权衡理论是研究资本结构的两种代表性理论观点。本文以2015-2020年中国上市公司为样本,构建面板数据分数回归模型,比较两种理论在我国资本市场的解释能力。研究结果表明,两种理论都不能完全解释中国资本市场的融资行为,上市公司的融资行为更接近于优序融资理论。

基于FARIMA模型的流量抽样测量方法

目前的流量抽样测量方法主要基于传统的数学理论,并没有考虑到实际网络流量的特征,基于此,提出基于FARIMA流量预测的抽样方法,根据流量预测值动态调整抽样率,既减轻了CPU的负载,又节省了存储空间。通过对比实际使用中的流量抽样测量方法取得的数据报文样本均值和Hurst参数,表明该方法能够正确体现原始数据的流量行为统计特征。

一类基于FARIMA过程的电梯导轨振动模型

研究电梯运行中导轨的随机振动时 ,将电梯运行中测得的导轨间距离DGB看作一时间序列 ,发现具有长相关性。常用的整数自回归模型、分数噪声模型都只能片面地描述该类数据的短相关性或长相关性。给出了利用FARIMA(自回归分数整合滑动平均模型 )拟合DGB的方法 ,该模型可同时刻画实测数据DGB的长相关和短相关特性 ,并通过对实测数据的实验 ,证明了模型的优效性。

基于组合优化算法的短期风电功率预测

针对风电功率的长记忆、大波动性特点,提出了一种短期风电功率组合预测算法。利用集合经验模式分解算法在风电功率序列分解过程中添加成对的正负噪声分量,得到的不同复杂度的子序列,提高信号重构精度和分解速度。风电功率子序列的线性分量应用自回归分数积分移动平均模型进行预测,风电功率子序列的非线性分量利用自回归分数积分移动平均模型的残差序列训练优化后的支持向量机模型来进行预测,最后组合得到风电功率预测结果。通过对国内某风电场风电功率数据进行验证,表明该组合预测模型的预测精度更高,且模型具有更好的适应性。

Retrieve Sea Surface Salinity Using Principal Component Regression Model Based on SMOS Satellite Data

The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

小波系数感知的网络流量预测机制

对网络流量的精确预测,可以准确把握网络运行趋势,及时防范网络故障。针对长期网络流量预测准确度低,收敛速度慢的问题,提出一种小波系数感知的网络流量预测(WCNTP)机制。借助重标极差(R/S)序列分析法初步评估网络流量在大时间尺度上的统计特性;利用离散小波变换将非平稳的网络流量分解为多个相对平稳的流量序列;利用分数自回归求和滑动(FARIMA)模型对网络流量进行预测。结果表明,所提机制在长期网络流量预测过程中,具有较高的准确度且收敛速度快,能够精确评估网络性能,在保证网络平稳运行的同时,提高网络服务质量。

Comprehensive modeling approach of axial ultrasonic vibration grinding force

The theoretical model of axial ultrasonic vibration grinding force is built on the basis of a mathematical model of cutting deforming force deduced from the assumptions of thickness of the undeformed debris under Rayleigh distribution and a mathematical model of friction based on the theoretical analysis of relative sliding velocity of abrasive and workpiece. Then, the coefficients of the ultrasonic vibration grinding force model are calculated through analysis of nonlinear regression of the theoretical model by using MATLAB, and the law of influence of grinding depth, workpiece speed, frequency and amplitude of the mill on the grinding force is summarized after applying the model to analyze the ultrasonic grinding force. The result of the above-mentioned law shows that the grinding force decreases as frequency and amplitude increase, while increases as grinding depth and workpiece speed increase; the maximum relative error of prediction and experimental values of the normal grinding force is 11.47% and its average relative error is 5.41%; the maximum relative error of the tangential grinding force is 10.14% and its average relative error is 4.29%. The result of employing regression equation to predict ultrasonic grinding force approximates to the experimental data, therefore the accuracy and reliability of the model is verified.

Deformation Behavior of Hot Isostatic Pressing FGH96 Superalloy

The deformation behavior of hot isostatic pressing （HIP） FGH96 superalloy was characterized in the temperature range of 1000-1100 ℃ and strain rate range of 0. 001-0. 1 s^-1 using hot compression testing. The flow curves of HIP FGH96, superalloy during hot deformation was analyzed systematically. The results show that deformation temperature, strain rate and strain are the main influence factors on flow stress of HIP FGH96 superalloy during hot deformation. The flow stress displays a peak at a critical strain and then decreases with further increase in strain. For a given strain, the flow stress decreases with the increase of deformation temperature, and increases with the increase of strain rate. A mathematical model of these flow curves was established through regression analysis and taking the strain as a modification factor. The calculated stress values agree well with the experimental values.

Hazard and population vulnerability analysis: a step towards landslide risk assessment

In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velocity ratio） and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources （Google Earth, aerial photographs and historical information）. Estimations of landslide susceptibility were determined by combining four statistical techniques： （i） logistic regression, （ii） quadratic discriminant analysis, （iii） linear discriminant analysis, and （iv） neuronal networks. A Digital Elevation Model （DEM） of lo m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment （SAVE） model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.

Coefficient of Partial Correlation and Its Calculation

This thesis offers the general concept of coefficient of partial correlation.Starting with regres-sion analysis,the paper,by using samples,infers the general formula of expressing coefficient of partial correlation by way of simple correlation coefficient.

Prediction of cavity growth rate during underground coal gasification using multiple regression analysis

During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

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.

Optimal control of cobalt crust seabedmining parameters based on simulated annealing genetic algorithm

Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.

IS China＇s Manufacturing Technica Efficiency Cost-push？

Do risingfactor prices promote technical efficiency （TE） in China＇s manufacturing sectors？ This topic has not yet been thoroughly studied in any literature. Using panel data from 1993 to 2007 of Chinese provinces＇ manufacturing industries, this paper calculated the TE by data envelopment analysis （DEA）. The result shows that China＇s manufacturing TE remained stagnate in the mid- and late-1990s and increased after 2000. Using a Pooled Regression Model, Fixed-effects Regression Model and Panel Corrected Standard Error （PCSE） to obtain robust parameters and the standard error, we find that rising factor prices have an obvious positive effect on the TE of the manufacturing industry and that this effect reaches its peak three to five years later. Therefore, the assumption of a ＂cost-Push TE＂ in China＇s manufacturing sectors can now be statistically proven. It has verified the significance of reforming China＇s faetor price system. Governments should value resource-allocating ej^iciency, implement policies that promote industrial transition and upgrading and transform the economic development pattern through technical innovation.

Optimizing crown density and volume estimation across two coniferous forest types in southern China via Boruta and Cubist methods

Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing.Based on GF-2 remote sensing data,sample plot survey data and forest resource survey data,this study used the Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.)and Pinus massoniana Lamb.as research objects to tackle the key challenges in the use of remote sensing technology.The Boruta feature selection technique,together with multiple stepwise and Cubist regression models,was used to estimate crown density and volume in portions of the research area’s stands,introducing novel technological methods for estimating stand parameters.The results show that:(i)the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable,which solves the problem of data and the loss of original feature data after dimensionality reduction;(ii)using the Cubist method to build the model yields better results than using multiple stepwise regression.The Cubist regression model’s coefficient of determination(R^(2))is all more than 0.67 in the Chinese fir plots and 0.63 in the P.massoniana plots.As a result,combining the two methods can increase the estimation accuracy of stand parameters,providing a theoretical foundation and technical support for future studies.

The nonparametric estimation of long memory spatio-temporal random field models

This paper considers the local linear estimation of a multivariate regression function and its derivatives for a stationary long memory(long range dependent) nonparametric spatio-temporal regression model.Under some mild regularity assumptions, the pointwise strong convergence, the uniform weak consistency with convergence rates and the joint asymptotic distribution of the estimators are established. A simulation study is carried out to illustrate the performance of the proposed estimators.

A moving average Cholesky factor model in joint mean-covariance modeling for longitudinal data

Modeling the mean and covariance simultaneously is a common strategy to efficiently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data. In this article, using generalized estimation equation techniques, we propose a new kind of regression models for parameterizing covariance structures. Using a novel Cholesky factor, the entries in this decomposition have moving average and log innovation interpretation and are modeled as the regression coefficients in both the mean and the linear functions of covariates. The resulting estimators for eovarianee are shown to be consistent and asymptotically normally distributed. Simulation studies and a real data analysis show that the proposed approach yields highly efficient estimators for the parameters in the mean, and provides parsimonious estimation for the covariance structure.

Adaptive quantile regression with precise risk bounds

An adaptive local smoothing method for nonpaxametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also al- lows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algo- rithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.

EMPIRICAL LIKELIHOOD FOR LONGITUDINAL PARTIALLY LINEAR MODEL WITH α-MIXING ERRORS

This paper considers large sample inference for the regression parameter in a partially linear regression model with longitudinal data and a-mixing errors. The authors introduce an estimated empirical likelihood for the regression parameter and show that its limiting distribution is a mixture of central chi-squared distributions. Also, the authors derive an adjusted empirical likelihood method which is shown to have a central chi-square limiting distribution. A simulation study is carried out to assess the performance of the empirical likelihood method.

Composite quantile regression estimation for P-GARCH processes

We consider the periodic generalized autoregressive conditional heteroskedasticity(P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator.The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions.The proposed methodology is also illustrated by Va R on stock price data.