scholarly journals Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data

Biometrics ◽  
2021 ◽  
Author(s):  
Lu Zhang ◽  
Sudipto Banerjee
Keyword(s):  
Spatial Factor ◽  
Misaligned Data ◽  
Factor Modeling ◽  
Matrix Normal

scholarly journals A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability

Biostatistics ◽  
2018 ◽  
Vol 20 (3) ◽  
pp. 468-484 ◽  
Author(s):  
Rachel C Nethery ◽  
Dale P Sandler ◽  
Shanshan Zhao ◽  
Lawrence S Engel ◽  
Richard K Kwok
Keyword(s):  
Climate Change ◽  
Factor Analysis ◽  
Spatial Data ◽  
Social Vulnerability ◽  
Social Characteristics ◽  
Analysis Model ◽  
Latent Factors ◽  
Factor Analysis Model ◽  
Spatial Factor ◽  
Misaligned Data

SummaryWith the threat of climate change looming, the public health community has an interest in identifying communities at the highest risk of devastation based not only on geographic features but also on social characteristics. Indices of community social vulnerability can be created by applying a spatial factor analysis to a set of relevant social variables measured for each community; however, current spatial factor analysis methodology is ill-equipped to handle spatially misaligned data. We introduce a joint spatial factor analysis model that can accommodate spatial data from two distinct partitions of a geographic space and identify a common set of latent factors underlying them. By defining the latent factors over the intersection of the two partitions, the model minimizes loss of information. Using simulated data constructed to mimic the spatial structure of our real data, we confirm the reliability of the model and demonstrate its superiority over competing ad hoc methods for dealing with misaligned data in spatial factor analysis. Finally, we construct an index of community social vulnerability for each census tract in Louisiana, a state prone to environmental disasters, which could be exacerbated by climate change, by applying the joint spatial factor analysis model to a set of misaligned social indicator data from the state. To demonstrate the utility of this index, we integrate it with Louisiana flood insurance claims data to identify communities that may be at particularly high risk during natural disasters, based on both social and geographic features.

scholarly journals Measuring Non-electoral Political Participation: Bi-factor Model as a Tool to Extract Dimensions

2021 ◽  
Author(s):  
Piotr Koc
Keyword(s):  
Political Participation ◽  
Factor Model ◽  
European Social Survey ◽  
Model Fit ◽  
One Dimension ◽  
Testing Procedures ◽  
Multiple Dimensions ◽  
Cast Doubt ◽  
Factor Modeling ◽  
Composite Scores

AbstractPolitical participation is a mainstay of political behavior research. One of the main dilemmas many researchers face pertains to the number of dimensions of political participation, i.e. whether we should model political participation as a unidimensional or multidimensional latent construct. Over the years, scholars usually have favored the solution with more than one dimension of political participation and they have backed the claim of multiple dimensions with a number of empirical tests. In this paper, I argue that the results from the frequently used testing procedures which rely on the model fit inspection and the Kaiser criterion can be very misleading and may yield in extracting too many dimensions. By employing bi-factor modeling to a European Social Survey dataset, I show that in a majority of countries political participation can be considered an essentially unidimensional latent quantity. I demonstrate that additional dimensions of political participation are very weak and unreliable and that we cannot regress them on external variables nor build composite scores based on them. These findings cast doubt on the conclusions of numerous previous studies where researchers modeled more than one dimension of political participation.

Hybrid fuzzy interface model of sports rehabilitation activities

2021 ◽  
pp. 1-10
Author(s):  
Wu Shoujiang
Keyword(s):  
Evaluation System ◽  
Clustering Algorithm ◽  
Mixed Model ◽  
Expectation Maximization Algorithm ◽  
Rehabilitation Training ◽  
Development Platform ◽  
Longitudinal Tracking ◽  
Database Structure ◽  
The Matrix ◽  
Matrix Normal

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.

scholarly journals Choosing Factors for the Vietnamese Stock Market

2021 ◽  
Vol 14 (3) ◽  
pp. 96
Author(s):  
Nina Ryan ◽  
Xinfeng Ruan ◽  
Jin E. Zhang ◽  
Jing A. Zhang
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Five Factor Model ◽  
Explanatory Power ◽  
Factor Models ◽  
Marginal Contribution ◽  
Return On Equity ◽  
Factor Modeling ◽  
Three Factor ◽  
Better Than

In this paper, we test the applicability of different Fama–French (FF) factor models in Vietnam, we investigate the value factor redundancy and examine the choice of the profitability factor. Our empirical evidence shows that the FF five-factor model has more explanatory power than the FF three-factor model. The value factor remains important after the inclusion of profitability and investment factors. Operating profitability performs better than cash and return-on-equity (ROE) profitability as a proxy for the profitability factor in FF factor modeling. The value factor and operating profitability have the biggest marginal contribution to a maximum squared Sharpe ratio for the five-factor model factors, highlighting the value factor (HML) non-redundancy in describing stock returns in Vietnam.

MULTI-FACTOR MODELING OF THE DEVELOPMENT OF PAID SERVICES TO THE POPULATION

2020 ◽  
pp. 70-79
Author(s):  
Ksenia V. Barmina ◽  
◽  
Marina V. Shinkevich ◽  
Farida F. Galimulina ◽  
◽  
...  
Keyword(s):  
Service Sector ◽  
Urban Infrastructure ◽  
Economic Systems ◽  
Innovative Development ◽  
Key Factors ◽  
Strategy Of Development ◽  
Russian Economy ◽  
Factor Modeling ◽  
Services Innovation ◽  
Relationship Of

The imbalance in the structure of the range of paid services provided to the population, caused by the COVID-19 pandemic, requires a flexible approach to management, identification of alternative business methods, and greater adaptation to the needs of the population. This trend determines measures to manage the urban infrastructure of public services, innovation and investment activities aimed at improving the efficiency of economic systems. The purpose of this study is to build a model for managing the development of paid services, taking into account the key factors of development, aimed at identifying the potential for improving the service sector. The goal was achieved by solving the following tasks: to diagnose the innovative development of paid services to the population; to build an economic and mathematical model for the development of paid services; to determine the direction of development of paid services to the population. The research methods used are comparison, analysis, synthesis, system approach, economic and mathematical modeling. The study identified features of innovative development of sphere of services, characterized by less active innovative activity on industrial production driven by economic factors; the model of «Three I» the development of services based on a mathematical relationship of infrastructure investment and innovative way to develop a balanced strategy of development of sphere of paid services to the population in the Russian economy. Based on this model, a set of recommendations can be formed to ensure business flexibility in the service sector in the conditions generated by the COVID-19 pandemic.

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