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Previous research has documented a positive effect of nonconformity on the inference of higher status in Western cultures. In Western culture, nonconformity is associated with high status, which is attributed to competence and autonomy. The present research extends research on nonconformity and status perception to the East Asian context by investigating how cultural tightness—the degree to which social norms are strict and deviance from norms is intolerable—at the personal level and national level jointly affects the evaluation of advertisements featuring nonconformity in two East Asian nations: Japan and China. Based on recent studies showing perceived level of tightness being higher in Japan than in China, we posit that consumers' evaluations of nonconformity in advertisements may differ between Japan and China, despite the two countries sometimes being grouped into a homogenous "East Asian" culture. Through four studies, we showed that Chinese consumers with low personal tightness had favorable attitudes toward ads portraying nonconformity and perceived products in these ads as having high status, whereas Chinese consumers with high personal tightness showed favorable attitudes toward ads portraying conformity. In contrast, Japanese consumers showed relatively harsher judgments for nonconforming behavior than Chinese consumers. [ABSTRACT FROM AUTHOR]

Nonstationary non-Gaussian spatial data are common in many disciplines, including climate science, ecology, epidemiology, and social sciences. Examples include count data on disease incidence and binary satellite data on cloud mask (cloud/no-cloud). Modeling such datasets as stationary spatial processes can be unrealistic since they are collected over large heterogeneous domains (i.e., spatial behavior differs across subregions). Although several approaches have been developed for nonstationary spatial models, these have focused primarily on Gaussian responses. In addition, fitting nonstationary models for large non-Gaussian datasets is computationally prohibitive. To address these challenges, we propose a scalable algorithm for modeling such data by leveraging parallel computing in modern high-performance computing systems. We partition the spatial domain into disjoint subregions and fit locally nonstationary models using a carefully curated set of spatial basis functions. Then, we combine the local processes using a novel neighbor-based weighting scheme. Our approach scales well to massive datasets (e.g., 2.7 million samples) and can be implemented in nimble, a popular software environment for Bayesian hierarchical modeling. We demonstrate our method to simulated examples and two massive real-world datasets acquired through remote sensing. [ABSTRACT FROM AUTHOR]

Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently developed functional shrinkage method. We apply our algorithm to survey and review datasets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants. for this article are available online. [ABSTRACT FROM AUTHOR]

Monte Carlo maximum likelihood (MCML) provides an elegant approach to find maximum likelihood estimators (MLEs) for latent variable models. However, MCML algorithms are computationally expensive when the latent variables are high-dimensional and correlated, as is the case for latent Gaussian random field models. Latent Gaussian random field models are widely used, for example, in building flexible regression models and in the interpolation of spatially dependent data in many research areas such as analyzing count data in disease modeling and presence-absence satellite images of ice sheets. We propose a computationally efficient MCML algorithm by using a projection-based approach to reduce the dimensions of the random effects. We develop an iterative method for finding an effective importance function; this is generally a challenging problem and is crucial for the MCML algorithm to be computationally feasible. We find that our method is applicable to both continuous (latent Gaussian process) and discrete domain (latent Gaussian Markov random field) models. We illustrate the application of our methods to challenging simulated and real data examples for which maximum likelihood estimation would otherwise be very challenging. Furthermore, we study an often overlooked challenge in MCML approaches to latent variable models: practical issues in calculating standard errors of the resulting estimates, and assessing whether resulting confidence intervals provide nominal coverage. Our study therefore provides useful insights into the details of implementing MCML algorithms for high-dimensional latent variable models. for this article are available online. [ABSTRACT FROM AUTHOR]

Inference for doubly intractable distributions is challenging because the intractable normalizing functions of these models include parameters of interest. Previous auxiliary variable MCMC algorithms are infeasible for multi-dimensional models with large data sets because they depend on expensive auxiliary variable simulation. We develop a fast Bayesian indirect algorithm by replacing an expensive auxiliary variable simulation from a probability model with a computationally cheap simulation from a surrogate model. We learn the relationship between the surrogate model parameters and the probability model parameters using Gaussian process approximations. We apply our methods to challenging examples, and illustrate that the algorithm addresses both computational and inferential challenges for doubly intractable distributions. Especially for a large social network model with 10 parameters, we show that our method can reduce computing time from about 2 weeks to 5 hours, compared to the previous method. [ABSTRACT FROM AUTHOR]

Doubly intractable distributions arise in many settings, for example, in Markov models for point processes and exponential random graph models for networks. Bayesian inference for these models is challenging because they involve intractable normalizing "constants" that are actually functions of the parameters of interest. Although several computational methods have been developed for these models, each can be computationally burdensome or even infeasible for many problems. We propose a novel algorithm that provides computational gains over existing methods by replacing Monte Carlo approximations to the normalizing function with a Gaussian process-based approximation. We provide theoretical justification for this method. We also develop a closely related algorithm that is applicable more broadly to any likelihood function that is expensive to evaluate. We illustrate the application of our methods to challenging simulated and real data examples, including an exponential random graph model, a Markov point process, and a model for infectious disease dynamics. The algorithm shows significant gains in computational efficiency over existing methods, and has the potential for greater gains for more challenging problems. For a random graph model example, we show how this gain in efficiency allows us to carry out accurate Bayesian inference when other algorithms are computationally impractical. for this article are available online. [ABSTRACT FROM AUTHOR]

AbstractComputer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors. [ABSTRACT FROM AUTHOR]

Models with intractable normalizing functions arise frequently in statistics. Common examples of such models include exponential random graph models for social networks and Markov point processes for ecology and disease modeling. Inference for these models is complicated because the normalizing functions of their probability distributions include the parameters of interest. In Bayesian analysis, they result in so-called doubly intractable posterior distributions which pose significant computational challenges. Several Monte Carlo methods have emerged in recent years to address Bayesian inference for such models. We provide a framework for understanding the algorithms, and elucidate connections among them. Through multiple simulated and real data examples, we compare and contrast the computational and statistical efficiency of these algorithms and discuss their theoretical bases. Our study provides practical recommendations for practitioners along with directions for future research for Markov chain Monte Carlo (MCMC) methodologists. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the abiotic transformation rate of 4-chlorophenol (4-CP) by manganese oxide. Sorption and desorption characteristics of polycyclic aromatic hydrocarbons (PAHs) on manganese oxide were also investigated in the presence of 4-CP. Results show that manganese oxide is effective for the transformation of 4-CP and initial reaction rate is the first-order with respect to the 4-CP and manganese oxide. Also, 4-CP transformation rates by manganese oxide are highly dependent upon solution pH and concentration of humic acid. At pH near the point of zero charge (PZC) of manganese oxide, the maximum reaction rate of 4-CP was observed. Sorption of phenanthrene on manganese oxide is significantly increased as a result of 4-CP transformation and subsequent generation of byproducts. Also, sorbed phenanthrene on manganese oxide in the presence of 4-CP showed high degree of desorption resistance. [ABSTRACT FROM AUTHOR]

The authors examined the verbal memory and visual memory of 19 patients with methamphetamine dependence and 18 normal controls to assess the memory impairment due to the use of methamphetamine. There was no significant difference in the verbal memory, but a significant difference was detected in the visual memory. The results that the methamphetamine selectively impairs visual memory seem to be because visual memory tasks are more sensitive to the damage of the executive function. [ABSTRACT FROM AUTHOR]