Use of Bivariate Dirichlet Process Mixture Spatial Model to Estimate Active Transportation-Related Crash Counts

2018 ◽  
Vol 2672 (38) ◽  
pp. 105-115 ◽  
Author(s):  
Wen Cheng ◽  
Gurdiljot Singh Gill ◽  
Tom Vo ◽  
Jiao Zhou ◽  
Taha Sakrani
Keyword(s):  
Dirichlet Process ◽  
Spatial Model ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Evaluation Criteria ◽  
Active Transportation ◽  
Parametric Models ◽  
Dirichlet Process Mixture ◽  
Pedestrian Crashes ◽  
The Impact

The current paper presents the comprehensive analysis of a bivariate Dirichlet process mixture spatial model for estimation of pedestrian and bicycle crash counts. This study focuses on active transportation at traffic analysis zone (TAZ) level by developing a semi-parametric model that accounts for the unobserved heterogeneity by combining the strengths of bivariate specification for correlation among crash modes; spatial random effects for the impact of neighboring TAZs; and Dirichlet process mixture for random intercept. Three alternate models, one Dirichlet and two parametric, are also developed for comparison based on different criteria. Bicycle and pedestrian crashes are observed to share three influential variables: the positive correlation of K12 student enrollment; the bike-lane density; and the percentage of arterial roads. The heterogeneity error term demonstrates the presence of statistically significant correlation among the bicycle and pedestrian crashes, whereas the spatial random effect term indicates the absence of a significant correlation for the area under focus. The Dirichlet models are consistently superior to non-Dirichlet ones under all evaluation criteria. Moreover, the Dirichlet models exhibit the capability to identify latent distinct subpopulations and suggest that the normal assumption of intercept associated with traditional parametric models does not hold true for the TAZ-level crash dataset of the current study.

Modeling Latent Information in Voting Data with Dirichlet Process Priors

2015 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Richard Traunmüller ◽  
Andreas Murr ◽  
Jeff Gill
Keyword(s):  
Dirichlet Process ◽  
Unobserved Heterogeneity ◽  
Vote Choice ◽  
Dirichlet Process Mixture ◽  
Omitted Variables ◽  
Factors Affecting ◽  
Structural Integration ◽  
Mixture Prior ◽  
Latent Effects ◽  
Improved Model

We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobserved heterogeneity among immigrant voters. Our approach is based ongeneralized linear mixed Dirichlet models(GLMDMs) where random effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account for unobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latent effects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficult problems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2) smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantive understanding of the factors affecting immigrants' turnout and vote choice. Once we account for unobserved heterogeneity among immigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extant literature suggests. When looking at vote choice, we also found that an immigrant's degree of structural integration does not affect the vote in favor of the CDU/CSU, a party that is traditionally associated with restrictive immigration policy.

scholarly journals Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

2021 ◽  
Author(s):  
Lucas Ondel
Keyword(s):  
Dirichlet Process ◽  
Experimental Results ◽  
Parametric Models ◽  
Dirichlet Process Mixture ◽  
Clustering Quality ◽  
Segmentation Accuracy ◽  
Target Data ◽  
Non Parametric

This work investigates subspace non-parametric models for the task of learning a set of acoustic units from unlabeled speech recordings. We constrain the base-measure of a Dirichlet-Process mixture with a phonetic subspace---estimated from other source languages---to build an \emph{educated prior}, thereby forcing the learned acoustic units to resemble phones of known source languages. Two types of models are proposed: (i) the Subspace HMM (SHMM) which assumes that the phonetic subspace is the same for every language, (ii) the Hierarchical-Subspace HMM (H-SHMM) which relaxes this assumption and allows to have a language-specific subspace estimated on the unlabeled target data. These models are applied on 3 languages: English, Yoruba and Mboshi and they are compared with various competitive acoustic units discovery baselines. Experimental results show that both subspace models outperform other systems in terms of clustering quality and segmentation accuracy. Moreover, we observe that the H-SHMM provides results superior to the SHMM supporting the idea that language-specific priors are preferable to language-agnostic priors for acoustic unit discovery.

scholarly journals Investment Decisions with Endogeneity: A Dirichlet Tree Analysis

2021 ◽  
Vol 14 (7) ◽  
pp. 299
Author(s):  
Mahsa Samsami ◽  
Ralf Wagner
Keyword(s):  
Dirichlet Process ◽  
Trading Volume ◽  
Positive Impact ◽  
Digital Marketing ◽  
Dirichlet Process Mixture ◽  
Endogenous Factors ◽  
Polya Trees ◽  
Marketing Variables ◽  
The Impact ◽  
Biased Estimates

Ignoring endogeneity when assessing investors’ decisions carries the risk of biased estimates for the influence of exogeneous marketing variables. This study shows how to overcome this challenge by using Pólya trees in the quantification of impacts on investors’ decisions. A total of 2255 investors recruited for this study received and opened a digital marketing newsletter about investing daily. Given the nature of investors’ decisions characterized by heterogeneity and endogeneity, the response model is assessed with the Dirichlet process mixture and estimated with the Markov chain Monte Carlo method. Digital marketing substantially exceeds the impact of investor experience, but both have a significant positive impact on investors’ trading volume. Findings obtained with the Dirichlet process mixture as a flexible model indicate that digital marketing even with latent endogenous factors makes an underlying contribution to the investors’ actions in the stock market.

scholarly journals Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

2021 ◽  
Author(s):  
Lucas Ondel
Keyword(s):  
Dirichlet Process ◽  
Experimental Results ◽  
Parametric Models ◽  
Dirichlet Process Mixture ◽  
Clustering Quality ◽  
Segmentation Accuracy ◽  
Target Data ◽  
Non Parametric

This work investigates subspace non-parametric models for the task of learning a set of acoustic units from unlabeled speech recordings. We constrain the base-measure of a Dirichlet-Process mixture with a phonetic subspace---estimated from other source languages---to build an \emph{educated prior}, thereby forcing the learned acoustic units to resemble phones of known source languages. Two types of models are proposed: (i) the Subspace HMM (SHMM) which assumes that the phonetic subspace is the same for every language, (ii) the Hierarchical-Subspace HMM (H-SHMM) which relaxes this assumption and allows to have a language-specific subspace estimated on the unlabeled target data. These models are applied on 3 languages: English, Yoruba and Mboshi and they are compared with various competitive acoustic units discovery baselines. Experimental results show that both subspace models outperform other systems in terms of clustering quality and segmentation accuracy. Moreover, we observe that the H-SHMM provides results superior to the SHMM supporting the idea that language-specific priors are preferable to language-agnostic priors for acoustic unit discovery.

scholarly journals Corporate Environmental Performance in China: The Moderating Effects of the Media versus the Approach of Local Governments

2020 ◽  
Vol 18 (1) ◽  
pp. 150
Author(s):  
Zhiru Guo ◽  
Chao Lu
Keyword(s):  
Environmental Protection ◽  
Environmental Performance ◽  
Local Governments ◽  
Random Effect ◽  
Random Effect Model ◽  
Corporate Environmental Performance ◽  
Media Attention ◽  
The Media ◽  
The Government ◽  
The Impact

This article selects the listed companies in China’s A-share heavy pollution industry from 2014 to 2018 as samples, uses a random effect model to empirically test the relationship between media attention and corporate environmental performance and examines the impacts of local government environmental protection and property nature on that relationship. Results are as follow: (1) Media attention can significantly affect a company’s environmental performance. The higher the media attention, the greater the company’s supervision and the better its environmental performance. (2) In areas where the government pays less attention to environmental protection, the impact of media on corporate environmental performance is more obvious, but in other areas, the impact of media on environmental performance cannot be reflected; (3) The media attention is very significant for the environmental performance improvement of state-owned enterprises, and it is not obvious in non-state-owned enterprises. (4) A further breakdown of the study found that the role of media attention in corporate environmental performance is only significant in the sample of local governments that have low environmental protection and are state-owned enterprises. This research incorporates the local government’s emphasis on environmental protection into the research field of vision, expands the research scope of media and corporate environmental performance, and also provides new clues and evidence for promoting the active fulfillment of environmental protection responsibilities by companies and local governments.

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

scholarly journals Assessing Architecture-and-Landscape Integration as a Basis for Evaluating the Impact of Construction Projects on the Cultural Landscape of Tourist Seaside Resorts

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Wojciech Bal ◽  
Magdalena Czalczynska-Podolska
Keyword(s):  
Construction Projects ◽  
Cultural Landscape ◽  
Negative Impact ◽  
Residential Buildings ◽  
Evaluation Criteria ◽  
Development Project ◽  
Clear Trend ◽  
Original Culture ◽  
Construction Plans ◽  
The Impact

The coastline of Western Pomerania has natural and cultural assets that have promoted the development of tourism, but also require additional measures to ensure the traditional features and characteristics are protected. This is to ensure that new developments conform to a more uniform set of spatial structures which are in line with the original culture. Today, seaside resorts are characterized by a rapid increase in development with a clear trend towards non-physiognomic architectural forms which continually expand and encroach on land closer to the coastline. This results in a blurring of the original concepts that characterized the founding seaside resort. This study evaluates 11 development projects (including a range of hotels, luxury residential buildings and hotel suites) built in 2009–2020 in the coastal area of Western Pomerania. An assessment of architecture-and-landscape integration for each development project was made, using four groups of evaluation criteria: aesthetic, socio-cultural, functional and locational factors. The study methodology included a historical and interpretative study (iconology, iconography, historiography) and an examination of architecture-and-landscape integration using a pre-prepared evaluation form. Each criterion was first assessed using both field surveys and desk research (including the analysis of construction plans and developer materials), and then compared with the original, traditional qualities of the town. This study demonstrates that it is possible to clearly identify the potential negative impact of tourism development on the cultural landscape of seaside resorts, and provides recommendations for future shaping, management and conservation of the landscape.

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