ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

用神经网络对开式冷挤压极限变形程度的预测

给出了基于 BP网络的极限变形程度预测的分析方法 ,得到了开式冷挤压εmax神经网络计算结果 ,与实验结果比较 ,其精度较高 ,证明了神经网络模型在开式冷挤压工艺中应用的可行性。     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

基于最大熵原理的政府投资工程委托代建风险研究

本文根据政府投资工程的风险特点,引入了熵的概念,建立了最大熵风险分析模型,利用熵权的特性求出各指标的客观权重,同时利用专家打分法得出各指标的主观权重,将两者结合作为各指标的综合权重,为进一步对风险指标的深入分析及风险决策研究奠定了良好的基础。最后,用实例验证了该模型的可行性和实用性。     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?     

RIDGE ESTIMATORS FOR PROBIT REGRESSION: WITH AN APPLICATION TO LABOUR MARKET DATA

In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.     

Estimating the gravity model when zero trade flows are frequent and economically determined

ABSTRACT

Since the 1950s, we have known that the presence of zero-valued dependent variables can seriously bias econometric estimates whether the zeros are included or excluded. Yet the widely-used gravity model is frequently estimated on samples that include large fractions of zeros. An influential paper by Santos Silva and Tenreyro – based on simulations that include no economically-determined zeros – concludes that the bias problems resulting from zeros and those resulting from heteroscedasticity and nonlinearity can be solved using the Poisson Pseudo-Maximum-Likelihood (PPML) model including the zero values. This paper begins by adapting the Santos Silva and Tenreyro experimental design to include economically-determined zeros to see whether this conclusion continues to hold. With this design, it finds that alternative estimators have lower bias than PPML. Changing to a Monte Carlo design that replicates the much-higher real-world frequency of predicted values near zero restores the finding of lower bias with the PPML estimator. The results highlight the need for very careful design of Monte Carlo experiments when evaluating alternative estimators of the gravity model.     

GAMBLING IN CONTESTS WITH REGRET

This paper discusses the gambling contest introduced in Seel and Strack (2013, Gambling in Contests, Journal of Economic Theory, 148(5), 2033–2048) and considers the impact of adding a penalty associated with failure to follow a winning strategy. The Seel and Strack model consists of n‐agents each of whom privately observes a transient diffusion process and chooses when to stop it. The player with the highest stopped value wins the contest, and each player's objective is to maximize her probability of winning the contest. We give a new derivation of the results of Seel and Strack based on a Lagrangian approach. Moreover, we consider an extension of the problem to a behavioral finance context in the sense of regret theory. In particular, an agent is penalized when her chosen strategy does not win the contest, but there existed an alternative strategy that would have resulted in victory.     

A true simulation study of three estimators at equal protection of respondents in randomized response sampling

In this study, three different estimators for estimating the proportion of a sensitive attribute in survey sampling are compared at equal protection of the respondents. The three estimators considered are due to Odumade and Singh (2009, Comm. Statist. Theory Methods) , Singh and Sedory (2011, Sociological Methods and Research) and a new estimator obtained by minimizing a chi‐squared distance. A SAS Macro is developed to compare these three estimators using a simulation study at equal protection of the respondents. A set of data from a real face‐to‐face interview was collected using two decks of cards and has been analyzed. The results are discussed.     

基于最大似然估计的分集信号中频合成方法

为了降低分集信号合成时输入信号的信噪比要求,提出了一种分集信号的中频合成方法 。该方法利用最大似然估计和迭代算法,通过计算输出合成信号和输入分集信号的相关值获 得合成权值,在中频实现信号合成。仿真和实验结果表明,该方法放宽了输入信号信噪比要 高于解调捕获门限这一要求,能自动实现最大合成增益。