Group Of Regression Models Research Articles

Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells.

The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models. Because the dataset has a limited number of samples, we utilized models that are effective with small datasets. Among different groups of regression models (linear, ensemble, and tree models), the ensemble models, specifically the LGB method, can achieve better performance. The results demonstrate accurate prediction of first spike latency, with an average mean squared error of 0.0002 and mean absolute error of 0.01 in 10-fold cross-validation. The research suggests the potential of machine learning in forecasting the first spike latency, allowing reliable estimation without the need for extensive animal testing. This intelligent predictive system facilitates efficient analysis of first spike latency changes in both healthy and unhealthy brain cells, streamlining experimentation and providing more detailed insights into the captured signals.

Expected credit loss modeling

This article proposes a method for modeling the probability of default, describes the statistical evaluation of the model, and presents a model of the software implementation algorithm. The algorithm automatically selects from the group of regression models where the models are both linear regression and various modifications of semi-logarithmic models and lag models for macro factors Xi,t,Xi,t-1, ...,Xi,t-TStatistical analysis is carried out using the coefficient of determination R-squared, p-value, VIF (variance inflation factor).The relevance of this topic is determined by the need for banking organizations to comply with international standards, such as International Financial Reporting Standards (IFRS 9) and the Agreement on Banking Supervision and Capital (Basel 3). These standards define credit risk assessment requirements and capital requirements. Adherence to these standards is important not only for ensuring the stability and reliability of the financial system, but also for maintaining the trust of clients and investors. Compliance with international standards also makes banks competitive in the global market and promotes investment inflows and the development of the financial sector.IFRS 9 can be presented in various mathematical models. The article proposes an approach to choosing the appropriate model for forecasting the probability of default. The described model selection method allows banks to choose the optimal default forecast assessment model within the framework of the given standard. This contributes to a more accurate and reliable assessment of credit risk, in accordance with regulatory requirements, which will provide banks with the means for better forecasting and management of financial resources, as well as risk reduction.The model selection methodology saves a significant amount of time and resources, since the search for the optimal model occurs automatically. This allows us to react more quickly to changes in the economic environment, improve decision-making strategies and manage credit risks, which is of great importance for financial institutions in a competitive environment.There is currently a war going on in Ukraine, and forecasting using current methods becomes a difficult task due to unpredictable stressful situations for the economy. In such conditions, standard models may not be sufficiently adapted to account for increased risk and volatility. The proposed approach allows finding more conservative forecasting models that can be useful in unstable periods and war.

Individual Tree Parameters Models for Melia Azedarach (Chinaberry) Tree Grown in Erbil

The crown width of a tree is very important parameter. It is responsible for tree survival and for producing the food for the whole tree. They produce oxygen, filter out dust and other airborne pollutants from the air, purification of the water, generate shadow and determine the scenic beauty of trees and forests. The tree crowns have a significant effect on the microclimate. But measuring of the crown width is a difficult task that needs much money, time and effort. Thus this study aimed at developing of mathematical relationship between the crown width and breast height diameter for Chinaberry trees grown in Sami Abdulrahman Park in Erbil, Iraq. Both crown width and breast height diameters are the most important parameters of a tree. The breast height diameter of a tree can be measured very easily using diameter tape or caliper, unlike measuring of the crown width which is more cumbersome. Therefore, it is accustomed to regress it with breast height diameter in mathematical equations.to be used for prediction of crown width instead of measuring it whenever it is needed. Such regression models were undergone many measures of precision in order to select the most appropriate one that best fits the collected dataset. In this study 50 regression models were developed, of which 25 included the y- intercept and the other 25 regression models was without Y-intercept. The first group of regression models were excluded from the competition list because of having low values of the coefficient of determination. The second group of equations were subjected to many criteria for the purpose of selecting the best one and at last the equation: was finally selected for its high prediction ability and simplicity in application. According to this equation the crown width of for Chinaberry trees grown in (Sami Abdulrahman Park in Erbil) increases with 1.50168m for each unit increase in .

Differential associations of homelessness with emergency department visits and hospitalizations by race, ethnicity, and gender.

To investigate the differential associations of homelessness with emergency department (ED) visits and hospitalizations by race, ethnicity, and gender. California Medicaid enrollment and claims. We identified beneficiaries experiencing homelessness (BEH) and those who did not (NBEH) using diagnosis and place of service codes and residential addresses. Outcomes include four ED visit measures and four hospitalization measures. We compared the use of these services by BEH to NBEH overall and by race, ethnicity, and gender groups in regression models controlling for covariates. We used a sample of Medicaid beneficiaries who met eligibility criteria for a California Medicaid demonstration program in 2017 and 2018 but were not enrolled in the program. We identified 473,069 BEH, and the rest (1,948,422) were considered NBEH. We used the 2018 data for utilization analyses and most covariates. We constructed lagged measures of health conditions using 2017 data. We found that homelessness was significantly associated with 0.34 more ED visits (p < 0.01) and a higher likelihood of frequent ED visits (2.77 percentage points [pp], p < 0.01), any ED visits due to mental health conditions (0.79 pp, p < 0.01), and any ED visits due to substance use disorders (1.47 pp, p < 0.01). Experiencing homelessness was also significantly associated with 0.03 more hospitalizations (p < 0.01), a higher likelihood of frequent hospitalizations (0.68 pp, p < 0.01) and high frequent hospitalizations (0.28 pp, p < 0.01), and a longer length of stay (0.53 days, p < 0.01). We found a larger association for American Indian and Alaska Native, Black, Native Hawaii or Pacific Islander, and White populations than that for Asian and Hispanic populations. The associations are larger for males than females. Our findings identified distinct utilization patterns by race, ethnicity, and gender. They indicated the need for developing race, ethnicity, and gender-specific strategies to reduce ED visits and hospitalizations of BEH.

The impact of different determinants on the dental caries experience of children living in Australia rural and urban areas.

Evidence suggests there are geographical variations in child oral health and this has prompted research into determinants of that variation. This study aimed to investigate factors attributable to the difference in child oral health between urban and rural areas in Australia. Data were from the National Child Oral Health Study 2012-14, a population-based study of 5- to 14-year-old children, who underwent oral epidemiological examinations by trained examiners. Caries prevalence (dmfs/DMFS>0) and experience (dmfs/DMFS count) in the primary dentition (5- to 8-year-old) and permanent dentition (9- to 14-year-old) were calculated. Children were grouped by residential location (urban or rural areas). A parental questionnaire collected information on family socio-economic factors, and individual health behaviours (dental access, sugar consumption and toothbrushing). Residential history was used to calculate lifetime exposure to water fluoridation (WF). Analyses were weighted to produce population-representative estimates. The primary outcomes were assessed separately for the two groups in regression models with robust standard error estimation to estimate prevalence ratios and mean ratios and their 95% confidence intervals. Population Attributable Fractions were calculated using the population distribution of the exposures and their adjusted estimates. 10581 5- to 8-year-old and 14041 9- to 14-year-old children were included. Caries prevalence was higher in rural than in urban areas. In multivariable models, exposure to fluoridation, reason for dental visit and consumption of sugary beverages were consistently associated with caries prevalence and experience. WF coverage attributed to differences in caries prevalence (10% vs 21%) and experience (14% vs 35%) in the permanent dentition. High consumption of sugary beverages attributed to a higher primary and permanent dental caries experience in rural than in urban areas. Dental access was also attributed to the differences between the two areas. Factors at both community and individual levels attributed to the observed differences in child caries prevalence and experience between urban and rural areas.

Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes

Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of regression coefficients across groups, the methods we propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using binary logit with two groups, we consider how our interpretive framework can be used with a broad class of regression models and can be extended to any number of groups.

Aqueous Humor Analysis Identifies Higher Branched Chain Amino Acid Metabolism as a Marker for Human Leukocyte Antigen-B27 Acute Anterior Uveitis and Disease Activity

Human leukocyte antigen-B27 (HLA-B27)-positive acute anterior uveitis (AAU) has a higher recurrence rate and shows more anterior chamber cell infiltration compared with HLA-B27-negative patients, suggesting distinct etiologies of these clinically overlapping conditions. To advance our understanding of the biology of AAU, we characterized the metabolic profile of aqueous humor (AqH) of patients with HLA-B27-associated AAU (B27-AAU) and noninfectious idiopathic AAU (idiopathic AAU). Experimental laboratory study. AqH samples from 2 independent cohorts totaling 30 patients with B27-AAU, 16 patients with idiopathic AAU, and 20 patients with cataracts underwent 2 individual rounds of direct infusion mass spectrometry. Features predicted by direct infusion mass spectrometry that facilitated maximum separation between the disease groups in regression models were validated by liquid chromatography/tandem mass spectrometry-based quantification with appropriate standards. Partial least square-discriminant analysis revealed metabolite profiles that were able to separate patients with B27-AAU from those with iodiopathic AAU. Pathway enrichment analysis, based on metabolites on which separation of the groups in the partial least square-discriminant analysis model was based, demonstrated the involvement of branched-chain amino acid biosynthesis, ascorbate and aldarate metabolism, the tricarboxylic acid cycle, and glycolysis-diverting pathways (eg, serine biosynthesis) across all investigated cohorts. Notably, the metabolite ketoleucine was elevated in B27-AAU across all 3 runs and moderately-but robustly-correlated with anterior chamber cell count (correlation coefficient range 0.41-0.81). These results illustrate metabolic heterogeneity between HLA-B27-positive and HLA-B27-negative AAU, including an increase of branched-chain amino acid biosynthesis, that reflects disease activity in AAU.

No Strings Attached: Evaluating an Unconditional Prenatal Income Supplement Using Linked Administrative Data

ABSTRACT&#x0D; ObjectivePerinatal outcomes have improved overall in developed countries over the last several decades but remain poor for disadvantaged populations. In Manitoba, Canada, the Healthy Baby Prenatal Benefit (HBPB), a cash transfer to low-income pregnant women, was introduced with the goal of improving perinatal outcomes. The objective of this study was to use linked administrative and program datasets to determine whether this unconditional income supplement was associated with improved birth outcomes for a low-income population.&#x0D; ApproachThis study used data from the PATHS Data Resource, which contains population-level health and social service records as well as program data for approximately 600,000 children in Manitoba, Canada, over a 30-year period, linkable at the individual level. All mother-newborn pairs, from 2003-2010, who met the following criteria were included for analyses: i) the mother was on social assistance (i.e., low income); ii) the infant was born in hospital; and, iii) the pair had a newborn risk screen (n=14,591), documenting factors such as prenatal alcohol and tobacco exposure and maternal and family characteristics. Low-income women who received HBPB (exposed, 10,738) were compared with low-income women who did not receive HBPB (unexposed, 3,853) on several outcomes: low birth weight, preterm, small- and large-for-gestational age, 5-minutes Apgar scores, breastfeeding initiation, neonatal readmission, and newborn hospital length of stay (LOS). Covariates from the risk screens were used to develop propensity scores to construct Inverse Probability of Treatment Weights, to balance differences between exposed and unexposed groups in regression models. Gamma sensitivity analyses assessed sensitivity to unmeasured confounding. Population attributable and preventable fractions were calculated.&#x0D; ResultsHBPB exposure was associated with reductions in low birth weight (adjusted risk ratio, aRR=0.71, 95% CI=0.63, 0.81), preterm (aRR=0.76 (0.69, 0.84)) and small-for-gestational age (aRR=0.90 (0.81, 0.99)) births and increases in breastfeeding (aRR=1.06 (1.03, 1.09)) and large-for-gestational age births (aRR=1.13 (1.05, 1.23)). For vaginal births, HBPB was associated with shortened LOS (mean=2.86, p&lt;0.0001). Results for breastfeeding, low birth weight, preterm and LOS were robust to unmeasured confounding. Reductions of 21% (95% CI 13.6, 28.3) of low birth weight and 17.5% (11.2, 23.8) of preterm were associated with receipt of the HBPB.&#x0D; ConclusionsA modest income supplement during pregnancy was associated with improved birth outcomes for infants born to low-income women. Placing conditions on income supplements to low income pregnant women may not be necessary to promote prenatal and perinatal health. Linked administrative data can be a valuable tool for program evaluation.

Using the finite element method and data mining techniques as an alternative method to determine the maximum load capacity in tapered roller bearings

Double-row tapered roller bearings (TRBs) are mechanical devices designed to support a combination of preload, radial load, axial load and torque. They are widely used in vehicles for high load and moderate rotation speeds. This combination of loads produces high contact stresses on the bearing raceways that are difficult to calculate, and can cause undesirable defects like fatigue spalling and pitting. In recent decades, the Finite Element Method (FEM) has been used to obtain the distribution of the contact stresses on each of the raceways, although this method has the disadvantage of a high computational cost. The myriad of possible combinations of input loads on the TRB (preload, radial load, axial load and torque) makes it much harder to calculate the distribution of these contact stresses. This paper proposes a methodology that combines the FEM and data mining techniques to determine the maximum load capacity in TRBs. First, a three-dimensional finite element (FE) model was generated according to the real materials' properties, geometry and coefficients of friction of all parts that make up the double-row TRB. Subsequently, a Design of Experiment (DoE) was completed that considered a combination of the mentioned input loads, which were simulated in the FE model. Based on the contact stresses obtained from the FE simulations, a group of regression models – linear regression (LR), Gaussian processes (GP), artificial neural networks (ANN), support vector machines (SVM) and regression trees (RT) – were built to predict the contact stresses ratios that act on each of the row of rollers in the outer raceway of the TRB. Finally, the best combination of input loads was achieved by applying evolutionary optimization techniques based on genetic algorithms (GA) to the best regression models previously obtained. The maximum load capacity of the TRB was achieved when the radial load obtained was a maximum, while the stresses ratios of the two contacts in the outer raceway of the TRB were close to 25%.

Unconditional Prenatal Income Supplement and Birth Outcomes.

Perinatal outcomes have improved in developed countries but remain poor for disadvantaged populations. We examined whether an unconditional income supplement to low-income pregnant women was associated with improved birth outcomes. This study included all mother-newborn pairs (2003-2010) in Manitoba, Canada, where the mother received prenatal social assistance, the infant was born in the hospital, and the pair had a risk screen (N = 14 591). Low-income women who received the income supplement (Healthy Baby Prenatal Benefit [HBPB], n = 10 738) were compared with low-income women who did not receive HBPB (n = 3853) on the following factors: low birth weight, preterm, small and large for gestational age, Apgar score, breastfeeding initiation, neonatal readmission, and newborn hospital length of stay (LOS). Covariates from risk screens were used to develop propensity scores and to balance differences between groups in regression models; γ sensitivity analyses were conducted to assess sensitivity to unmeasured confounding. Population-attributable and preventable fractions were calculated. HBPB was associated with reductions in low birth weight (aRR, 0.71 [95% CI, 0.63-0.81]), preterm births (aRR, 0.76 [95% CI, 0.69-0.84]) and small for gestational age births (aRR, 0.90 [95% CI, 0.81-0.99]) and increases in breastfeeding (aRR, 1.06 [95% CI, 1.03-1.09]) and large for gestational age births (aRR, 1.13 [95% CI, 1.05-1.23]). For vaginal births, HBPB was associated with shortened LOS (weighted mean, 2.86; P < .0001). Results for breastfeeding, low birth weight, preterm birth, and LOS were robust to unmeasured confounding. Reductions of 21% (95% CI, 13.6-28.3) for low birth weight births and 17.5% (95% CI, 11.2-23.8) for preterm births were associated with HBPB. Receipt of an unconditional prenatal income supplement was associated with positive outcomes. Placing conditions on income supplements may not be necessary to promote prenatal and perinatal health.

Intraday and intraweek trade anomalies on the Czech stock market

The paper examines intraday and intraweek market returns on the Czech stock market for the search of time and seasonal anomalies in its activities during the last ten years. Existence or absence of anomalies indicates the efficiency of the market. A group of regression models and GARCH (1,1) model is used for the analysis of daily and high frequency data of the PX index. Time varying nature of market seasonalities is revealed with the Czech equity market having implications for changing efficiency over the studied period, when the Czech Republic’s accession to the EU implied the increase in efficiency and the global financial crisis led to opposite results and regularities, which are not yet fully overcomed. Additionally, significant hour-of-the-day effect (open jump effect) in the index returns is established.

Non-parametric short-term load forecasting

Load forecasting is an important problem in the operation and planning of electrical power generation, as well as in transmission and distribution networks. This paper is interested by short-term load forecasting. It deals with the development of a reliable and efficient Kernel regression model to forecast the load in the Hydro Québec distribution network. A set of past load history comprising of weather information and load consumption is used. A non-parametric model serves to establish a relationship among past, current and future temperatures and the system loads. The paper proposes a class of flexible conditional probability models and techniques for classification and regression problems. A group of regression models is used, each one focusing on consumer classes characterising specific load behaviour. Each forecasting process has the information of the past 300 h and yields estimated loads for next 120 h. Numerical investigations show that the suggested technique is an efficient way of computing forecast statistics.