On approximate likelihood inference in a poisson mixed model

A two-step estimation approach is proposed for the fixed-effect parameters, random effects and their variance σ² of a Poisson mixed model. In the first step, it is proposed to construct a small σ²-based approximate likelihood function of the data and utilize this function to estimate the fixed-effect parameters and σ². In the second step, the random effects are estimated by minimizing their posterior mean squared error. Methods of Waclawiw and Liang (1993) based on so-called Stein-type estimating functions and of Breslow and Clayton (1993) based on penalized quasilikelihood are compared with the proposed likelihood method. The results of a simulation study on the performance of all three approaches are reported.     

阻力墙参数对转向节锻件充填影响的神经网络分析

为研究阻力墙结构参数对转向节锻件长叉充填的定量影响,设计了广义回归( GR)人工神经网络模型。用“舍一法”训练了模型,并采用3个样本对模型进行预测检验,散点表明预测值和实验值拟合较好。统计学指标为：均方误差M1为0. 898 0,相对均方误差M2为0.167 0%,拟合分值V为1.973 9,说明人工神经网络具有较高的预测精度。最后用 神经网络分析阻力墙关键参数对锻件长叉充填的定量影响,结果表明：长叉侧边桥部宽度和阻力墙斜度的增加对长叉充填作用不明显;阻力墙间隙的加大不利于长叉充填;阻力墙宽度对长叉充填的影响呈抛物线关系,先增大后减小,存在一个极大值。GR人工神经网络模型能够定量预测各阻力墙参数对长叉充填的影响。     

Influence flows in the academy: Using affiliation networks to assess peer effects among researchers

Little is known about how influence flows in the academy, because of inherent difficulties in collecting data on large samples of friendship and advice-seeking networks over time. We propose taking advantage of the relative abundance of “affiliation network” data to assess aggregate patterns of how individual and dyadic characteristics channel influence among researchers. We formulate and test our approach using new data on 2034 faculty members at Stanford University over a 15-year period, analyzing different affiliations as potential influence channels for changes in grant productivity. Results indicate that research productivity is more malleable to ongoing interpersonal influence processes than suggested in prior research: a strong, salient tie to a colleague in an authority position is most likely to transmit influence, and most forms of influence are likely to spill over to behaviors outside those jointly produced by collaborators. However, the genders and institutional locations of ego-alter pairs significantly affect how influence flows.     

The relationship between neighborhood racial concentration and verbal ability: An investigation using the institutional resources model

Relatively few studies examine the relationship between racial residential segregation and educational or cognitive outcomes. Using data from the National Longitudinal Study of Adolescent Health and the institutional resources model of neighborhood effects, I investigate one account of how macrostructural arrangements between race, neighborhood segregation, and school quality interact to produce inequalities in test scores. Consistent with the institutional resources model, results suggest that school quality varies across neighborhoods based, in part, on their degree of racial concentration. Indeed, school quality and other school characteristics mediate the relationship between racial concentration and verbal skills, particularly among black males. These findings have implications not only for inequalities in cognitive skills among blacks across residential space, but also between blacks and whites given high levels of residential segregation in the United States. In sum, findings illustrate yet another way in which residential segregation contributes to, and not merely reflects, racial inequalities.     

Trend effects on perceived avalanche hazard

Hazard-level forecasts constitute an important risk mitigation tool to reduce loss of economic values and human life. Avalanche forecasts represent an example of this. As for many other domains, avalanche risk is communicated using a color-coded, categorical risk scale aimed at informing the public about past, current, and future risk. We report the results from three experiments in which we tested if an irrelevant past trend in forecasted avalanche danger affects perceptions of current and future avalanche risk. Our sample consisted of individuals from three different populations targeted by national avalanche warning services. All three experiments showed that the perception of avalanche risk is influenced by the trend, but that the effect is opposite for perceptions of current and expectations of future avalanche risk. While future avalanche risk is extrapolated in the same direction as the change from the previous day, we found that perceived current risk appears to be based on an average of past and current risk. These effects diminish when we provide participants with a scale indicating the exact level of avalanche danger. For most of our measurement instruments, however, the effects remain significant. These results imply that targeted populations may consider historic information more than was intended by the sender. As such, our results have implications for both avalanche warning services and risk communication in general.     

直接融资结构对经济增长的非线性影响

伴随着中国经济的高速发展,实体经济结构与金融结构难以匹配的问题日益严重,大量中小企业日益增长的融资需求无法得到满足。股权融资与债券融资结构优化问题成为企业融资决策的普遍难题。本文致力于研究直接融资结构即股权融资和债券融资的比例对于中国经济增长的影响。首先,本文通过内生增长理论模型研究了直接融资结构与经济增长之间的动态关系。然后,基于2002-2019年间中国的相关数据,对技术创新变量进行中介效应检验,验证直接融资结构对于经济增长的作用路径。研究发现：直接融资结构对中国经济增长的影响呈现非线性效应;直接融资结构是通过促进技术创新来进一步促进经济增长;目前,中国仍处于需要提高股权融资占比来促进经济快速增长的阶段。     

The Time Series and Cross‐Section Asymptotics of Dynamic Panel Data Estimators

In this paper we derive the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N→ 0 the fixed T results for GMM and LIML remain valid, but WG, although consistent, has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM, and LIML estimators exhibit negative asymptotic biases of order 1/T, 1/N, and 1/(2N−T), respectively. In addition, the crude GMM estimator that neglects the autocorrelation in first differenced errors is inconsistent as T/N→c>0, despite being consistent for fixed T. Finally, we discuss the properties of a random effects pseudo MLE with unrestricted initial conditions when both T and N tend to infinity.     

Does intergenerational mobility shape psychological distress? Sorokin revisited

Drawing from Sorokin's hypothesis that socially mobile individuals are at greater risk of experiencing psychological distress than their non-mobile counterparts, we investigate whether intergenerational occupational mobility influences psychological distress, as measured by the Center for Epidemiologic Studies Depression (CES-D) scale. Using data for men from the Wisconsin Longitudinal Study (WLS) and Sobel's Diagonal Mobility Models, we find little evidence for Sorokin's hypothesis; mobile individuals are no more likely to be psychologically distressed than their non-mobile counterparts. In fact, one group of mobile men - those who left their farming origins - are actually less distressed than the sons who remain as farmers and non-mobile men in higher-ranked social classes. We speculate that this reflects the fact that farming became very arduous during the late 20th century and these mobile sons of farmers appreciate their improved life chances. Our findings suggest that the association between mobility and psychological distress varies across specific class backgrounds and is contingent upon the broader social and economic context.     

Embedding latent class regression and latent class distal outcome models into cluster-weighted latent class analysis: a detailed simulation experiment

Usually in latent class (LC) analysis, external predictors are taken to be cluster conditional probability predictors (LC models with external predictors), and/or score conditional probability predictors (LC regression models). In such cases, their distribution is not of interest. Class-specific distribution is of interest in the distal outcome model, when the distribution of the external variables is assumed to depend on LC membership. In this paper, we consider a more general formulation, that embeds both the LC regression and the distal outcome models, as is typically done in cluster-weighted modelling. This allows us to investigate (1) whether the distribution of the external variables differs across classes, (2) whether there are significant direct effects of the external variables on the indicators, by modelling jointly the relationship between the external and the latent variables. We show the advantages of the proposed modelling approach through a set of artificial examples, an extensive simulation study and an empirical application about psychological contracts among employees and employers in Belgium and the Netherlands.     

Regularization in dynamic random-intercepts models for analysis of longitudinal data

This paper addresses the problem of simultaneous variable selection and estimation in the random-intercepts model with the first-order lag response. This type of model is commonly used for analyzing longitudinal data obtained through repeated measurements on individuals over time. This model uses random effects to cover the intra-class correlation, and the first lagged response to address the serial correlation, which are two common sources of dependency in longitudinal data. We demonstrate that the conditional likelihood approach by ignoring correlation among random effects and initial responses can lead to biased regularized estimates. Furthermore, we demonstrate that joint modeling of initial responses and subsequent observations in the structure of dynamic random-intercepts models leads to both consistency and Oracle properties of regularized estimators. We present theoretical results in both low- and high-dimensional settings and evaluate regularized estimators' performances by conducting simulation studies and analyzing a real dataset. Supporting information is available online.