Assessing Total Factor Productivity Growth in Sub‐Saharan African Agriculture

The importance of the agricultural sector in the economic development process is well known. Improvements in agricultural productivity are often found to spill into other areas of a developing economy, potentially improving the standards of living of urban and rural workers alike. Given the importance of this sector, accurate measures of total factor productivity (TFP) across countries can be helpful in identifying conditions, institutions or policies that promote agricultural development. In this article, we estimate TFP growth in agriculture for a panel of 39 sub‐Saharan African countries from 1961 to 2007. We also develop a set of development outcome measures theoretically consistent with strong agricultural performance to serve as external validation of our results. We find that three estimation methods (stochastic frontier, generalised maximum entropy, and Bayesian efficiency) generate relative rankings that are consistent with the development outcome measures, providing external validation of the methods. However, the data envelopment analysis approach performs poorly in this regard.     

Reparameterized inverse gamma regression models with varying precision

In this article, we propose a mean linear regression model where the response variable is inverse gamma distributed using a new parameterization of this distribution that is indexed by mean and precision parameters. The main advantage of our new parametrization is the straightforward interpretation of the regression coefficients in terms of the expectation of the positive response variable, as usual in the context of generalized linear models. The variance function of the proposed model has a quadratic form. The inverse gamma distribution is a member of the exponential family of distributions and has some distributions commonly used for parametric models in survival analysis as special cases. We compare the proposed model to several alternatives and illustrate its advantages and usefulness. With a generalized linear model approach that takes advantage of exponential family properties, we discuss model estimation (by maximum likelihood), black further inferential quantities and diagnostic tools. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the obtained results. A real application using minerals data set collected by Department of Mines of the University of Atacama, Chile, is considered to demonstrate the practical potential of the proposed model.     

Identification and Estimation of Club Convergence Models with Spatial Dependence

This article deals with heterogeneity and spatial dependence in economic growth analysis by developing a two‐stage strategy that identifies clubs by a mapping analysis and estimates a club convergence model with spatial dependence. Since estimation of this class of convergence models in the presence of regional heterogeneity poses both identification and collinearity problems, we develop an entropy‐based estimation procedure that simultaneously takes account of ill‐posed and ill‐conditioned inference problems. The two‐step strategy is applied to assess the existence of club convergence and to estimate a two‐club spatial convergence model across Italian regions over the period 1970 to 2000.     

管理精细化价值最大化——以管理精细化促发电企业实现价值最大化

发电企业作为技术密集型和资本密集型重点行业,在其不断发展壮大的过程中,逐步形成了一套科学、完整的管理体系和实践经验。但随着电力市场竞争加剧,发电企业原有的粗放式经营管理体系越来越难以满足市场竞争的需求。本文通过对精细化管理理念的引入,就精细化管理相对与发电企业的意义,及发电企业中实施精细化管理的方法进行了探讨,从而为发电企业实施精细化管理提供借鉴。     

Repeated measures and longitudinal data analysis using higher‐order entropies

This paper implements the generalized maximum entropy (GME) method in longitudinal data setup to investigate the regression para meters and correlation among the repeated measurements. We derive the GME system using Shannon classical entropy as well as some higher‐order entropies assuming an autoregressive correlation structure. This method is illustrated using two simulated examples to study the effect of changing the support range and compare the performance of the GME approach with the classical estimation methods.     

频率选择性慢衰落信道下的快速频率估计

给出了在频率选择性慢衰落信道下适用于突发传输的两种快速频偏估计算法。针对慢衰落选择性信道下的频率估计问题，两种算法均给出了一个简单的闭式解，且算法实现无需任何信道幅度信息。第一种算法要求用于频率估计的训练序列的自相关矩阵具有对角特性，第二种算法使用周期的训练序列，两种算法所需的运算量大大低于最大似然估计算法。仿真结果显示，在较高信噪比下两种算法均具有较好的估计性能。     

一种低复杂度的CPM信号最大似然块检测算法

为了降低连续相位调制(Continuous Phase Modulation,CPM)信号多符号非相干检测的运算复杂度，提出了一种低复杂度的最大似然块检测算法。该算法充分利用已判决输出的符号对检测过程中的符号向量取值进行约束，有效减少判决统计量计算时的运算量，进而降低算法复杂度。另外，该算法引入判决长度变量，通过调整单次检测时判决符号数使算法能够在检测性能与运算量之间灵活折中。仿真结果表明，提出的低复杂度检测算法能够适用于全响应和部分响应CPM信号，相比原最大似然块检测算法能够在不损失检测性能的前提下降低算法运算量至少50〖WT《Times New Roman》〗%〖WTBZ〗，并且能够通过选择不同的判决长度提高算法应用的灵活性。     

振动压实试验方法在无机结合料配合比设计中的应用

无机结合料稳定材料振动压实试验方法适用于粗集料含量较大的稳定材料。本文结合连霍高速路面加宽工程介绍了水泥稳定基层配合比的振动压实试验设计方法及特点。     

Multi-sample simple step-stress experiment under time constraints

In the context of accelerated life testing, a step-stress model allows for testing under different conditions at various intermediate stages of the experiment. The goal is to develop inference for the mean lifetime at each stress level. The maximum likelihood estimates (MLEs) exist only when some (at least one) failures are observed at each stress level. This limitation can be tackled by a multi-sample step-stress model, which imposes a weaker condition for the existence of the MLEs, i.e. at each stress level, some failures (at least one) must be observed in at least one of the samples. The step-stress experiment with multiple samples at the same stress levels was introduced by Kateri et al. ( Journal of Statistical Planning and Inference, 139 , 2009a ). In this article, we focus on the likelihood inference under such a multi-sample set-up for the case of a simple step-stress experiment under exponentially distributed lifetimes when time constraints are in place in the experimentation.     

高校学生社团可持续发展研究

大学生社团是活跃校园文化的重要载体,它能充分发挥学生课外学习的自主积极性,从而促进课内教学的效果,是第二课堂不可或缺的组成部分。本文通过对高校学生社团重要性,现状及存在问题的分析,探讨高校学生社团建设的几点措施,从而有利于发挥其最大效度,更好的为教学服务,为培养复合型人才服务。