statistical model
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Author(s):  
H.-W. Chen

Abstract. A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.


BMC Nutrition ◽  
2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Bilal Shikur Endris ◽  
Geert-Jan Dinant ◽  
Seifu H. Gebreyesus ◽  
Mark Spigt

Abstract Background The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model. Methods We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization’s cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05. Result The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4–59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14–1.39), wasting (OR = 1.35, 95% BCI (1.15–1.57), maternal anemia (OR = 1.61, 95% BCI (1.44–1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08–1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62–0.85). Conclusion Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.


2022 ◽  
Vol 70 (2) ◽  
pp. 3903-3918
Author(s):  
Junaid Tariq ◽  
Ayman Alfalou ◽  
Amir Ijaz ◽  
Hashim Ali ◽  
Imran Ashraf ◽  
...  

2022 ◽  
pp. 111845
Author(s):  
Eoghan McKenna ◽  
Jessica Few ◽  
Ellen Webborn ◽  
Ben Anderson ◽  
Simon Elam ◽  
...  

2022 ◽  
Vol 52 (3) ◽  
Author(s):  
Daniela Maria Martin ◽  
Renata Franciéli Moraes ◽  
Maria Christine Rizzon Cintra ◽  
Claudete Reisdorfer Lang ◽  
Alda Lúcia Gomes Monteiro ◽  
...  

ABSTRACT: Temperament often depends on the animals’ reaction to people, social and environmental conditions. However, little is known about the influence of changes in the pasture environment on cattle temperament. Thus, this study was designed to evaluate if an animals’ temperament changes in response to being kept in a silvopastoral system. This study evaluated the effect of the tree components in a pasture environment on the temperament of any grazing cattle in integrated crop-livestock systems. A total of thirty-two Angus steers were allocated to either a livestock (L) or livestock-forest (LF) system and observed from December 2019 to February 2020. Each animal was evaluated for their reactivity score, flight speed, and number of vocalizations. The statistical model established that the animals were random effects and that the treatments and periods were fixed effects using the MIXED procedure, and the means were compared using LSMeans. The flight speed and number of vocalizations were similar in both production systems, while the reactivity score was lower for animals kept in the LF system when compared to those in the L system. This suggested that the LF system interferes positively with the animal’s temperament in relation to the L production system. However, additional research is needed to understand the influence of the production system on animal temperament.


2022 ◽  
pp. 510-538
Author(s):  
Ismail Elhassnaoui ◽  
Zineb Moumen ◽  
Hicham Ezzine ◽  
Marwane Bel-lahcen ◽  
Ahmed Bouziane ◽  
...  

In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.


Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen

AbstractEnsuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.


2021 ◽  
Vol 23 (1) ◽  
pp. 326
Author(s):  
Oleg V. Kondrashov ◽  
Sergey A. Akimov

Antimicrobial peptides (AMPs) are considered prospective antibiotics. Some AMPs fight bacteria via cooperative formation of pores in their plasma membranes. Most AMPs at their working concentrations can induce lysis of eukaryotic cells as well. Gramicidin A (gA) is a peptide, the transmembrane dimers of which form cation-selective channels in membranes. It is highly toxic for mammalians as being majorly hydrophobic gA incorporates and induces leakage of both bacterial and eukaryotic cell membranes. Both pore-forming AMPs and gA deform the membrane. Here we suggest a possible way to reduce the working concentrations of AMPs at the expense of application of highly-selective amplifiers of AMP activity in target membranes. The amplifiers should alter the deformation fields in the membrane in a way favoring the membrane-permeabilizing states. We developed the statistical model that allows describing the effect of membrane-deforming inclusions on the equilibrium between AMP monomers and cooperative membrane-permeabilizing structures. On the example of gA monomer-dimer equilibrium, the model predicts that amphipathic peptides and short transmembrane peptides playing the role of the membrane-deforming inclusions, even in low concentration can substantially increase the lifetime and average number of gA channels.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
M. Nagy ◽  
Ehab M. Almetwally ◽  
Ahmed M. Gemeay ◽  
Heba S. Mohammed ◽  
Taghreed M. Jawa ◽  
...  

This paper aims to introduce a superior discrete statistical model for the coronavirus disease 2019 (COVID-19) mortality numbers in Saudi Arabia and Latvia. We introduced an optimal and superior statistical model to provide optimal modeling for the death numbers due to the COVID-19 infections. This new statistical model possesses three parameters. This model is formulated by combining both the exponential distribution and extended odd Weibull family to formulate the discrete extended odd Weibull exponential (DEOWE) distribution. We introduced some of statistical properties for the new distribution, such as linear representation and quantile function. The maximum likelihood estimation (MLE) method is applied to estimate the unknown parameters of the DEOWE distribution. Also, we have used three datasets as an application on the COVID-19 mortality data in Saudi Arabia and Latvia. These three real data examples were used for introducing the importance of our distribution for fitting and modeling this kind of discrete data. Also, we provide a graphical plot for the data to ensure our results.


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