arXiv subjects

Quanli Wang

Publications and source records attributed to Quanli Wang.

4 recordsLinked to original sources

arXiv2025

Sustainable intensification of small-scale aquaculture systems depends on the local context and characteristics of producers

Aquaculture has been the fastest growing food production sector globally due to its potential to improve food security, stimulate economic growth, and reduce poverty. Its rapid development has been linked to sustainability challenges, many of which are still unresolved and poorly understood. Small-scale producers account for an increasing fraction of aquacultural output. At the same time, many of these producers experience poverty, food insecurity, and rely on unimproved production practices. We develop a stylized mathematical model to explore the effects of ecological, social, and economic factors on the dynamics of a small-scale pond aquaculture system. Using analytical and numerical methods, we explore the stability, asymptotic dynamics, and bifurcations of the model. Depending on the characteristics of the system, the model exhibits one of three distinct configurations: monostability with a global poverty trap in a nutrient-dominated or fish-dominated system; bistability with poverty trap and well-being attractors; multistability with poverty trap and two well-being attractors with different characteristics. The model results show that intensification can be sustainable only if it takes into account the local social-ecological context. In addition, the heterogeneity of small-scale aquaculture producers matters, as the effects of intensification can be unevenly distributed among them. Finally, more is not always better because too high nutrient input or productivity can lead to a suboptimal attractor or system collapse.

q-bio.PE↗

arXiv2021

Multiple Imputation and Synthetic Data Generation with the R package NPBayesImputeCat

In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular at statistical agencies, the respondent-level data they collect from surveys and censuses can suffer from high rates of missingness. Furthermore, agencies are obliged to protect respondents' privacy when publishing the collected data for public use. The NPBayesImputeCat R package, introduced in this paper, provides routines to i) create multiple imputations for missing data, and ii) create synthetic data for statistical disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet process mixture of products of multinomial distributions model used in the package, and illustrate various uses of the package using data samples from the American Community Survey (ACS). We also compare results of the missing data imputation to the mice R package and those of the synthetic data generation to the synthpop R package.

stat.CO↗

arXiv2016

Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data

We present a Bayesian model for estimating the joint distribution of multivariate categorical data when units are nested within groups. Such data arise frequently in social science settings, for example, people living in households. The model assumes that (i) each group is a member of a group-level latent class, and (ii) each unit is a member of a unit-level latent class nested within its group-level latent class. This structure allows the model to capture dependence among units in the same group. It also facilitates simultaneous modeling of variables at both group and unit levels. We develop a version of the model that assigns zero probability to groups and units with physically impossible combinations of variables. We apply the model to estimate multivariate relationships in a subset of the American Community Survey. Using the estimated model, we generate synthetic household data that could be disseminated as redacted public use files with high analytic validity and low disclosure risks. Supplementary materials for this article are available online.

stat.ME↗

arXiv2015

Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression

Multi-subject functional magnetic resonance imaging (fMRI) data has been increasingly used to study the population-wide relationship between human brain activity and individual biological or behavioral traits. A common method is to regress the scalar individual response on imaging predictors, known as a scalar-on-image (SI) regression. Analysis and computation of such massive and noisy data with complex spatio-temporal correlation structure is challenging. In this article, motivated by a psychological study on human affective feelings using fMRI, we propose a joint Ising and Dirichlet Process (Ising-DP) prior within the framework of Bayesian stochastic search variable selection for selecting brain voxels in high-dimensional SI regressions. The Ising component of the prior makes use of the spatial information between voxels, and the DP component groups the coefficients of the large number of voxels to a small set of values and thus greatly reduces the posterior computational burden. To address the phase transition phenomenon of the Ising prior, we propose a new analytic approach to derive bounds for the hyperparameters, illustrated on 2- and 3-dimensional lattices. The proposed method is compared with several alternative methods via simulations, and is applied to the fMRI data collected from the KLIFF hand-holding experiment.

stat.AP↗