Approximate Bayesian inference in semi-mechanistic models

Inference of interaction networks represented by systems of differential equations is a challenging problem in many scientific disciplines. In the present article, we follow a semi-mechanistic modelling approach based on gradient matching. We investigate the extent to which key factors, including the kinetic model, statistical formulation and numerical methods, impact upon performance at network reconstruction. We emphasize general lessons for computational statisticians when faced with the challenge of model selection, and we assess the accuracy of various alternative paradigms, including recent widely applicable information criteria and different numerical procedures for approximating Bayes factors. We conduct the comparative evaluation with a novel inferential pipeline that systematically disambiguates confounding factors via an ANOVA scheme.     

Heterogeneous climate effects on human migration in Indonesia

We examine the effect of anomalous temperatures, rainfall levels, and monsoon timing on migration outcomes in Indonesia. Using panel data from the Indonesian Family Life Survey and high-resolution climate data, we assess whether intra- and inter-province moves are used as a response to climatic shocks. We evaluate the relative importance of temperature, rainfall, and monsoon timing for migration. Only temperature and monsoon timing have significant effects, and these do not operate in the direction commonly assumed. Estimated effects vary according to individuals’ gender, membership in a farm household, and location. We also analyze climate effects on sources of household income, which highlights the multi-phasic nature of household responses. Results undermine narratives of a uniform global migratory response to climate change and highlight the heterogeneous use of migration as a response to such changes. By extending previous research on environmentally induced migration in Indonesia, we also highlight the sensitivity of estimates to alternative climate and migration measures.     

Disruptive Organisation

Organizations are driven by continuous change. Global, socio-economic and technological megatrends – demographic changes and digitalization – are the reasons for transformation. Traditional organization forms are not in line with customers‘ need. Disruptive modes of development and innovations have been intensively discussed elsewhere. The topic of disruptive organization have not been discussed towards internal and external customer relations, although of high relevance. 129 Managers answered on communication ways and the purpose of upcoming meaning for the working place/office. Two new models and five concrete recommendations have been established for upcoming future organization.     

Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.     

Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics

The use of Bayesian nonparametrics models has increased rapidly over the last few decades driven by increasing computational power and the development of efficient Markov chain Monte Carlo algorithms. We review some applications of these models in economic applications including: volatility modelling (using both stochastic volatility models and GARCH-type models) with Dirichlet process mixture models, uses in portfolio allocation problems, long memory models with flexible forms of time-dependence, flexible extension of the dynamic Nelson-Siegel model for interest rate yields and multivariate time series models used in macroeconometrics.     

A penalized likelihood method for nonseparable space–time generalized additive models

In this paper, we study space–time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve the estimation of the response and smooth terms of GAMs. The results show that our space–time generalized additive models estimated response and smooth terms reasonable well, and in addition, the mean squared error, mean absolute deviation and coverage intervals improved considerably compared to the classic GAM. An application on particulate matter concentration in the North-Italian region of Piemonte is also presented.     

Control or Autonomy? A Case Study of two Organizational Forms in the Zhangjiakou Internal Migrant Organization

Control often weakens autonomy in organizational theories. While many scholars focus on the paradox of control and autonomy, few pay attention to how an organization obtains both control and autonomy. By exploring Zhangjiakou Internal Immigrant Organization, this study finds two mechanisms can contribute to the balanced combination of control and autonomy. First, managers play ambidextrous official-merchant roles, which means superiors take both bureaucratic and autonomous management styles. Second, members have dual organizational goals for both political status and network support. In conclusion, the successful combination is shaped by the subjective interaction between the government, superior managers and subordinate members.     

Yes Academic Bias is a Problem and We Need to Address it: a Response to Larregue

Larregue argued that while there is an ideological imbalance among academics that this imbalance has not unfairly impacted our findings. He also implies that it is the activism of conservatives, rather than progressives, which has created real problems in academia. As such he disputes claims that activism among progressive academics has create a backlash from political conservatives. However, Larregue does not address the previous empirical work on the subject of academic bias. Furthermore, his argument of methodological rigor does not address the tendency of scholars to overlook alternate theories. His argument that progressive scholars are no more biased than conservative scholars fails to account for the sheer number of progressive scholars relative to conservative scholars as it concerns the possibly of institutional bias. I also find his focus on fraud to be misplaced since scholars do not have to engage in fraud to be in ideological silos that do not take alternative theories into account. As to his second major argument, I am unsure whether liberal academic activism leads to conservative mistrust and await further future research on this topic. I do commend Larregue in bringing the question of sources of conservative mistust to our attention.     

Learning from others: conditioning versus averaging

How should we revise our beliefs in response to the expressed probabilistic opinions of experts on some proposition when these experts are in disagreement? In this paper I examine the suggestion that in such circumstances we should adopt a linear average of the experts’ opinions and consider whether such a belief revision policy is compatible with Bayesian conditionalisation. By looking at situations in which full or partial deference to the expressed opinions of others is required by Bayesianism I show that only in trivial circumstances are the requirements imposed by linear averaging compatible with it.     

Funding Civil Society? Bilateral Government Support for Development NGOs

Research on the determinants of foreign aid tends to focus on the relationship between donor country priorities and recipient state characteristics, but donors also make decisions about which organizations and programs within countries will receive assistance. Although NGOs increasingly have been recipients of foreign aid, few data are available to investigate which organizations within a given country receive that funding. Donors may prioritize structural characteristics of NGOs or their local ties—or they may seek a combination that blends concern about efficiency and accountability with an interest in developing national civil society. We use original data from Cambodia to explore whether aid is likely to go to managerial organizations (professionalized NGOs and NGOs that utilize modern management tools) or to organizations that are embedded in the domestic context. We argue that managerialism provides legitimacy for NGOs by signaling capacity and accountability to donors, increasing the likelihood of government funding. We argue that local embeddedness also confers legitimacy by aligning community ties and networks to rights-based development, increasing the likelihood of government funding. We find general support for the managerialism argument, but donor agencies do not prioritize direct funding for “indigenous” NGOs—not even among those with high levels of managerialism.