如何对餐饮业成本进行控制

随着我国市场经济的逐步确立和人民生活水平的日益提高,我国的餐饮业也得到了快速发展。要搞好餐饮经营,成本控制是关键。本文从成本控制的意义、成本控制存在的问题和如何进行成本控制这三个方面进行了阐述,特别是在如何进行成本控制上,较全面、系统地分析了成本控制的一般方法。在全球经济一体化和日益激烈的市场竞争下,希望本文能为我国餐饮业的壮大发展做出一点贡献。     

机械臂非线性定位方程计算新方法

针对机械臂定位算法中,数值算法计算量大,仿真过程存在累积误差,几何算法通用性不强等问题,以手术机器人六自由度机械臂为研究对象,建立了一组多关节机械臂非线性定位方程组,提出了其基于非线性大范围渐近稳定的求解方法。结果表明该方法既简化运算,又有利于物理实现,为机械臂高速、准确运动提供了基础,实验表明该算法适用多关节的移动机械臂的非线性定位方程的求解,有较高的求解精度和收敛速度。     

Locally and maximin optimal designs for multi-factor nonlinear models

This paper considers the search for locally and maximin optimal designs for multi-factor nonlinear models from optimal designs for sub-models of a lower dimension. In particular, sufficient conditions are given so that maximin D-optimal designs for additive multi-factor nonlinear models can be built from maximin D-optimal designs for their sub-models with a single factor. Some examples of application are models involving exponential decay in several variables.     

Spectral analysis of Markov switching GARCH models with statistical inference

We derive matrix expressions in closed form for the autocovariance function and the spectral density of Markov switching GARCH models and their powers. For this, we apply the Riesz–Fischer theorem which defines the spectral representation as the Fourier transform of the autocovariance function. Under suitable assumptions, we prove that the sample estimator of the spectral density is consistent and asymptotically normally distributed. Further statistical implications in terms of order identification and parameter estimation are discussed. A simulation study confirms the validity of the asymptotic properties. These methods are also well suited for financial market applications, and in particular for the analysis of time series in the frequency domain, as shown in some proposed real-world examples.     

The US–China trade war and Phase One agreement

The Trump administration changed US trade policy toward China in ways that will take years for researchers to sort out. This paper makes four specific contributions to that research agenda. The first is to carefully mark the timing, definitions, and scale of the products subject to the tariff changes affecting US–China trade from January 20, 2017 through January 20, 2021. One result was each country increasing its average duty on the other to rates of roughly 20 percent, with the new tariffs and counter-tariffs covering more than 50 percent of bilateral trade. The second contribution is to highlight two additional channels through which bilateral tariffs changed during this period that received less research attention. One tariff change is through product exclusions, another is trade remedy policies of antidumping and countervailing duties. The third contribution is to provide an initial exploration into why China fell more than 40 percent short of meeting the goods purchase commitments set out under the first year of the Phase One agreement. The last contribution is to consider additional trade policy actions—involving forced labor, export controls for reasons of national security or human rights, and reclassification of trade with Hong Kong—likely to affect US–China trade beyond the Trump administration.     

Forecasting interest rates: a comparative assessment of some second-generation nonlinear models

Modeling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary methods such as ARMA and VAR, but only with moderate success. We examine here three methods, which account for several specific features of the real world asset prices such as nonstationarity and nonlinearity. Our three candidate methods are based, respectively, on a combined wavelet artificial neural network (WANN) analysis, a mixed spectrum (MS) analysis and nonlinear ARMA models with Fourier coefficients (FNLARMA). These models are applied to weekly data on interest rates in India and their forecasting performance is evaluated vis-à-vis three GARCH models [GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)] as well as the random walk model. Both the WANN and MS methods show marked improvement over other benchmark models, and may thus hold out several potentials for real world modeling and forecasting of financial data.     

On the inferential use of predictive sample reuse

The predicitive sample reuse (PSR) data analysis technique proposed by Geisser and Eddy (1979) is applied to the analysis of categorical data. This application yiclds a new approach which has a number of advantages over classical methods for analysis of such data. A PSR technique for comparing linear or nonlinar regressino relationships, for two or more populations, and a PSR ailernative to certain nonparaemetri statistical tesis are also proposed.     

Least absolute value estimation in regression models: an annotated bibliography

This paper presents a comprehensive listing of articles on least absolute value (LAV) estimation as applied to linear and non-linear regression models and in systems of equations. References to the LAV method as applied in approximation theory are also included. Annotations describing the content of each article follow each reference.     

转型经济下市场导向型战略影响因素分析

本文通过建立一个概念模型论证了国有企业在转型经济时期采用市场导向型战略的效果以及采用该战略的影响因素。并通过对274份有效问卷的分析验证了我们的模型,分析结果表明政府对企业经营管理干预的减少、公司治理结构的完善、战略控制都有利于市场导向型战略。结果还显示了市场导向型战略在改善国有企业经营业绩上的显著性。这些发现和对原有理论的改进能够帮助我们更好的制定改革政策和国有企业的战略。