3mm波相参脉冲放大链

根据３ ｍ ｍ 波段相参脉冲雷达对发射机的要求,拟定了基波注入锁定３ ｍ ｍ 波谐波振荡器、３ｍ ｍ 波连续波雪崩振荡器和两级脉冲雪崩振荡器级联注入锁定的放大链路方案,提出并实施了“脉间异频防泄漏技术”,抑制了漏信号对系统工作的有害影响,实现了当输入电平为２ ｍ Ｗ 的６ ｍ ｍ 波信号时,３ ｍ ｍ波输出脉冲峰值功率大于５ Ｗ,脉宽约３０ ｎｓ。     

On the Global Convergence of Stochastic Fictitious Play

We establish global convergence results for stochastic fictitious play for four classes of games: games with an interior ESS, zero sum games, potential games, and supermodular games. We do so by appealing to techniques from stochastic approximation theory, which relate the limit behavior of a stochastic process to the limit behavior of a differential equation defined by the expected motion of the process. The key result in our analysis of supermodular games is that the relevant differential equation defines a strongly monotone dynamical system. Our analyses of the other cases combine Lyapunov function arguments with a discrete choice theory result: that the choice probabilities generated by any additive random utility model can be derived from a deterministic model based on payoff perturbations that depend nonlinearly on the vector of choice probabilities.     

Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

This paper proposes a new nested algorithm (NPL) for the estimation of a class of discrete Markov decision models and studies its statistical and computational properties. Our method is based on a representation of the solution of the dynamic programming problem in the space of conditional choice probabilities. When the NPL algorithm is initialized with consistent nonparametric estimates of conditional choice probabilities, successive iterations return a sequence of estimators of the structural parameters which we call K–stage policy iteration estimators. We show that the sequence includes as extreme cases a Hotz–Miller estimator (for K=1) and Rust's nested fixed point estimator (in the limit when K→∞). Furthermore, the asymptotic distribution of all the estimators in the sequence is the same and equal to that of the maximum likelihood estimator. We illustrate the performance of our method with several examples based on Rust's bus replacement model. Monte Carlo experiments reveal a trade–off between finite sample precision and computational cost in the sequence of policy iteration estimators.     

基于区间灰数与离散灰数双重异构序列的预测建模方法研究

系统变化的复杂性导致了系统行为数据的不确定性与异构性,面向多源信息的数据集结导致了表征系统变化规律的灰色异构时序数据的产生。对面向区间灰数与离散灰数的双重异构数据序列预测建模方法展开研究,通过对区间灰数均匀分割处理,得到与离散灰数灰元数量相等的次级区间灰数,进而实现了灰色异构数据的"同质化"转换;在此基础上构建了面向异构数据序列的灰色预测模型,并应用该模型实现了大桥沉降量的有效模拟与准确预测。研究成果对拓展灰色预测模型应用范围具有积极意义。     

Flexible parametric estimation technique for a competing risks model with unobserved heterogeneity: a Monte Carlo study

We analyse a flexible parametric estimation technique for a competing risks (CR) model with unobserved heterogeneity, by extending a local mixed proportional hazard single risk model for continuous duration time to a local mixture CR (LMCR) model for discrete duration time. The state-specific local hazard function for the LMCR model is per definition a valid density function if we have either one or two destination states. We conduct Monte Carlo experiments to compare the estimated parameters of the LMCR model, and to compare the estimated parameters of a CR model based on a Heckman–Singer-type (HS-type) technique, with the data-generating process parameters. The Monte Carlo results show that the LMCR model performs better or at least as good as the HS-type model with respect to the estimated structure parameters in most of the cases, but relatively poorer with respect to the estimated duration-dependence parameters.     

Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables

This paper develops a method for inference in dynamic discrete choice models with serially correlated unobserved state variables. Estimation of these models involves computing high‐dimensional integrals that are present in the solution to the dynamic program and in the likelihood function. First, the paper proposes a Bayesian Markov chain Monte Carlo estimation procedure that can handle the problem of multidimensional integration in the likelihood function. Second, the paper presents an efficient algorithm for solving the dynamic program suitable for use in conjunction with the proposed estimation procedure.     

The Effect of Expected Income on Individual Migration Decisions

This paper develops a tractable econometric model of optimal migration, focusing on expected income as the main economic influence on migration. The model improves on previous work in two respects: it covers optimal sequences of location decisions (rather than a single once‐for‐all choice) and it allows for many alternative location choices. The model is estimated using panel data from the National Longitudinal Survey of Youth on white males with a high‐school education. Our main conclusion is that interstate migration decisions are influenced to a substantial extent by income prospects. The results suggest that the link between income and migration decisions is driven both by geographic differences in mean wages and by a tendency to move in search of a better locational match when the income realization in the current location is unfavorable.     

提高离散数学课程课堂教学有效性方法的研究与实践

文章首先分析了数理逻辑在离散数学课程中的重要性和特殊性,以及它的课程特点。接着以其为例,探讨和研究了提高离散数学课堂教学有效性方法的问题,提出了具体的方法和措施,最后通过具体实例对方法和措施的效果进行了说明。本文的目的是为了提高课堂教学的质量,激发学生的学习兴趣,夯实学生的学习基础,培养学生的创新能力。     

承重加气混凝土开裂性能试验研究

粉煤灰蒸压加气混凝土砌块,是国内外公认的适应建筑节能的新型环保墙体材料之一。用它砌筑墙体能减轻建筑物的自重,降低综合造价,但是较易开裂。制品表面产生裂纹,严重影响后期性能。本文在对其机理进行分析的基础上,通过加入纤维对粉煤灰蒸压加气混凝土的开裂问题予以试验研究,较好解决了这一技术难题。     

Limited Information and Advertising in the U.S. Personal Computer Industry

Traditional discrete‐choice models assume buyers are aware of all products for sale. In markets where products change rapidly, the full information assumption is untenable. I present a discrete‐choice model of limited consumer information, where advertising influences the set of products from which consumers choose to purchase. I apply the model to the U.S. personal computer market where top firms spend over $2 billion annually on advertising. I find estimated markups of 19% over production costs, where top firms advertise more than average and earn higher than average markups. High markups are explained to a large extent by informational asymmetries across consumers, where full information models predict markups of one‐fourth the magnitude. I find that estimated product demand curves are biased toward being too elastic under traditional models. I show how to use data on media exposure to improve estimated price elasticities in the absence of micro ad data.