Co-integration Rank Determination in Partial Systems Using Information Criteria

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

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Empirical comparison of skewed t-copula models for insurance and financial data

Model Assisted Statistics and Applications ◽

10.3233/mas-200506 ◽

2020 ◽

Vol 15 (4) ◽

pp. 351-361

Author(s):

Liwei Huang ◽

Arkady Shemyakin

Keyword(s):

Bayesian Estimation ◽

Goodness Of Fit ◽

Model Performance ◽

Information Criteria ◽

Stock Index ◽

Likelihood Method ◽

Special Importance ◽

Copula Model ◽

Technical Problems ◽

T Copula

Skewed t-copulas recently became popular as a modeling tool of non-linear dependence in statistics. In this paper we consider three different versions of skewed t-copulas introduced by Demarta and McNeill; Smith, Gan and Kohn; and Azzalini and Capitanio. Each of these versions represents a generalization of the symmetric t-copula model, allowing for a different treatment of lower and upper tails. Each of them has certain advantages in mathematical construction, inferential tools and interpretability. Our objective is to apply models based on different types of skewed t-copulas to the same financial and insurance applications. We consider comovements of stock index returns and times-to-failure of related vehicle parts under the warranty period. In both cases the treatment of both lower and upper tails of the joint distributions is of a special importance. Skewed t-copula model performance is compared to the benchmark cases of Gaussian and symmetric Student t-copulas. Instruments of comparison include information criteria, goodness-of-fit and tail dependence. A special attention is paid to methods of estimation of copula parameters. Some technical problems with the implementation of maximum likelihood method and the method of moments suggest the use of Bayesian estimation. We discuss the accuracy and computational efficiency of Bayesian estimation versus MLE. Metropolis-Hastings algorithm with block updates was suggested to deal with the problem of intractability of conditionals.

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Nonlinear predictive model selection and model averaging using information criteria

Systems Science & Control Engineering ◽

10.1080/21642583.2018.1496042 ◽

2018 ◽

Vol 6 (1) ◽

pp. 319-328 ◽

Cited By ~ 4

Author(s):

Yuanlin Gu ◽

Hua-Liang Wei ◽

Michael M. Balikhin

Keyword(s):

Model Selection ◽

Predictive Model ◽

Model Averaging ◽

Information Criteria

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Plasma protein expression profiles, cardiovascular disease, and religious struggles among South Asians in the MASALA study

Scientific Reports ◽

10.1038/s41598-020-79429-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Long H. Ngo ◽

M. Austin Argentieri ◽

Simon T. Dillon ◽

Blake Victor Kent ◽

Alka M. Kanaya ◽

...

Keyword(s):

Cardiovascular Disease ◽

Plasma Protein ◽

Expression Profiles ◽

South Asians ◽

Predictive Biomarkers ◽

Information Criteria ◽

Blood Protein ◽

Complement Factor ◽

Factor B ◽

Complement Factor B

AbstractBlood protein concentrations are clinically useful, predictive biomarkers of cardiovascular disease (CVD). Despite a higher burden of CVD among U.S. South Asians, no CVD-related proteomics study has been conducted in this sub-population. The aim of this study is to investigate the associations between plasma protein levels and CVD incidence, and to assess the potential influence of religiosity/spirituality (R/S) on significant protein-CVD associations, in South Asians from the MASALA Study. We used a nested case–control design of 50 participants with incident CVD and 50 sex- and age-matched controls. Plasma samples were analyzed by SOMAscan for expression of 1305 proteins. Multivariable logistic regression models and model selection using Akaike Information Criteria were performed on the proteins and clinical covariates, with further effect modification analyses conducted to assess the influence of R/S measures on significant associations between proteins and incident CVD events. We identified 36 proteins that were significantly expressed differentially among CVD cases compared to matched controls. These proteins are involved in immune cell recruitment, atherosclerosis, endothelial cell differentiation, and vascularization. A final multivariable model found three proteins (Contactin-5 [CNTN5], Low affinity immunoglobulin gamma Fc region receptor II-a [FCGR2A], and Complement factor B [CFB]) associated with incident CVD after adjustment for diabetes (AUC = 0.82). Religious struggles that exacerbate the adverse impact of stressful life events, significantly modified the effect of Contactin-5 and Complement factor B on risk of CVD. Our research is this first assessment of the relationship between protein concentrations and risk of CVD in a South Asian sample. Further research is needed to understand patterns of proteomic profiles across diverse ethnic communities, and the influence of resources for resiliency on proteomic signatures and ultimately, risk of CVD.

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A foci cohort analysis to monitor successful and persistent foci under Thailand’s Malaria Elimination Strategy

Malaria Journal ◽

10.1186/s12936-021-03648-8 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Prayuth Sudathip ◽

Suravadee Kitchakarn ◽

Jui A. Shah ◽

Donal Bisanzio ◽

Felicity Young ◽

...

Keyword(s):

Malaria Elimination ◽

Proportional Hazards ◽

Proportional Hazards Model ◽

Forest Disturbance ◽

Cohort Analysis ◽

Information Criteria ◽

Factors Associated ◽

Malaria Burden ◽

Hazards Model ◽

Autochthonous Transmission

Abstract Background Thailand’s success in reducing malaria burden is built on the efficient “1-3-7” strategy applied to the surveillance system. The strategy is based on rapid case notification within 1 day, case investigation within 3 days, and targeted foci response to reduce the spread of Plasmodium spp. within 7 days. Autochthonous transmission is still occurring in the country, threatening the goal of reaching malaria-free status by 2024. This study aimed to assess the effectiveness of the 1-3-7 strategy and identify factors associated with presence of active foci. Methods Data from the national malaria information system were extracted from fiscal years 2013 to 2019; after data cleaning, the final dataset included 81,012 foci. A Cox’s proportional hazards model was built to investigate factors linked with the probability of becoming an active focus from 2015 to 2019 among foci that changed status from non-active to active focus during the study period. We performed a model selection technique based on the Akaike Information Criteria (AIC). Results The number of yearly active foci decreased from 2227 to 2013 to 700 in 2019 (68.5 %), and the number of autochthonous cases declined from 17,553 to 3,787 (78.4 %). The best Cox’s hazard model showed that foci in which vector control interventions were required were 18 % more likely to become an active focus. Increasing compliance with the 1-3-7 strategy had a protective effect, with a 22 % risk reduction among foci with over 80 % adherence to 1-3-7 timeliness protocols. Other factors associated with likelihood to become or remain an active focus include previous classification as an active focus, presence of Plasmodium falciparum infections, level of forest disturbance, and location in border provinces. Conclusions These results identified factors that favored regression of non-active foci to active foci during the study period. The model and relative risk map align with the national malaria program’s district stratification and shows strong spatial heterogeneity, with high probability to record active foci in border provinces. The results of the study may be useful for honing Thailand’s program to eliminate malaria and for other countries aiming to accelerate malaria elimination.

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On the BIC for determining the number of control points in B-spline surface approximation in case of correlated observations

Journal of Geodetic Science ◽

10.1515/jogs-2020-0110 ◽

2020 ◽

Vol 10 (1) ◽

pp. 110-123

Author(s):

Gaël Kermarrec ◽

Hamza Alkhatib

Keyword(s):

Shape Control ◽

Simple Procedure ◽

Spline Approximation ◽

Autoregressive Process ◽

Optimal Number ◽

Information Criteria ◽

General Effect ◽

Control Points ◽

B Spline ◽

Correlated Observations

Abstract B-spline curves are a linear combination of control points (CP) and B-spline basis functions. They satisfy the strong convex hull property and have a fine and local shape control as changing one CP affects the curve locally, whereas the total number of CP has a more general effect on the control polygon of the spline. Information criteria (IC), such as Akaike IC (AIC) and Bayesian IC (BIC), provide a way to determine an optimal number of CP so that the B-spline approximation fits optimally in a least-squares (LS) sense with scattered and noisy observations. These criteria are based on the log-likelihood of the models and assume often that the error term is independent and identically distributed. This assumption is strong and accounts neither for heteroscedasticity nor for correlations. Thus, such effects have to be considered to avoid under-or overfitting of the observations in the LS adjustment, i.e. bad approximation or noise approximation, respectively. In this contribution, we introduce generalized versions of the BIC derived using the concept of quasi- likelihood estimator (QLE). Our own extensions of the generalized BIC criteria account (i) explicitly for model misspecifications and complexity (ii) and additionally for the correlations of the residuals. To that aim, the correlation model of the residuals is assumed to correspond to a first order autoregressive process AR(1). We apply our general derivations to the specific case of B-spline approximations of curves and surfaces, and couple the information given by the different IC together. Consecutively, a didactical yet simple procedure to interpret the results given by the IC is provided in order to identify an optimal number of parameters to estimate in case of correlated observations. A concrete case study using observations from a bridge scanned with a Terrestrial Laser Scanner (TLS) highlights the proposed procedure.

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Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

Educational and Psychological Measurement ◽

10.1177/0013164421992407 ◽

2021 ◽

pp. 001316442199240

Author(s):

Chunhua Cao ◽

Eun Sook Kim ◽

Yi-Hsin Chen ◽

John Ferron

Keyword(s):

Root Mean Square ◽

Interaction Effect ◽

Intraclass Correlation ◽

Information Criteria ◽

Parameter Estimates ◽

Mean Square ◽

Fit Indices ◽

Cluster Number ◽

Multiple Indicator ◽

The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

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