AIC Value Research Articles

The Regime Examination of Nigeria Exchange Rate Volatility: Evidence from Markov Regime Switching Autoregressive Approach

Inarguably, the escalation in dollar rates and the price instability in the Nigerian economy underwent significant structural and institutional changes. In assessing the importance of understanding exchange rates, it becomes imperative to build reliable models for predicting the volatility of exchange rates of home currency. Hence, this study aims to model the Nigerian exchange rate volatility using the Markov regime-switching model. The study analyses the Nigerian exchange rate returns in two and three distinct regimes by employing the Markov regime-switching autoregressive (MS-AR) model with data from 2nd January 2018 to 7th September 2020. Four MS-AR candidate models were estimated for the exchange rate series. Based on the least AIC value, MS(3)-AR(2) was returned as the most parsimonious model among the four candidate models. The MS(3)-AR(2) analysis established a high probability that the returns system remains in the liquidation and awareness states. It implied that only unconventional or severe events could switch the series from regime 2 (liquidation phase) and regime 3 (awareness). While there is a low probability that the system will stay in an imbalanced regime implies high switching of regime 1. Furthermore, an average duration period of 2 days, six days and five days were estimated for the imbalance, liquidation and awareness regimes, respectively. Thus, the findings, i.e. imbalance and liquidation regimes’ identification and their average durations, show that the Naira in the foreign exchange market is not favourable for investors to trade. The study recommends that the Nigerian government should direct more efforts towards improving the performance of the Naira in the foreign exchange market to make the market more favourable for investors. Specifically, the CBN should develop new strategies towards tackling the behaviour of the Nigerian exchange rate when in a liquidation state.

Zero-inflated poisson regression untuk memodelkan faktor-faktor yang mempengaruhi terjadinya kebakaran di kabupaten Sidoarjo

The purpose of this research is determine the factors that affect the rate of fires in Sidoarjo using analysis Zero-Inflated Poisson (ZIP) regression. The procedure in this research through the steps are detecting the distribution of a variable number of fire (Y), identified cases multikolinearitas, establishing models Poison regression, testing overdispersi or underdispersi, establishing models ZIP regression, testing coefficient simultaneously and partially, and choose a suitable model. The independent variable used are population density (X_1), the number of factories (X_2), sugarcane and paddy land area (X_3), vacant land that overgrown with weeds (X_4), and year (X_5) Based on the regression model Zero-Inflated Poisson (ZIP) obtained best model of rate of fires that affected by the population density (X_1), sugarcane and paddy land area (X_3), and the year (X_5) to distinguish the difference the incidence of fires in every year. The model has a value of AIC (257,4) and the smallest value of R^2 (70,86).

Early Growth and Development and Nonlinear Model Fitting Analysis of Ashidan Yak.

Understanding animal growth plays an important role in improving animal genetics and breeding. In order to explore the early growth and development law of Ashidan yak, the body weight (BW), wither height (WH), body oblique length (BL) and chest girth (CG) of 260 female Ashidan yaks were measured. These individuals grew under grazing conditions, and growth traits were measured at 6, 12, 18 and 30 months of age. Then the absolute growth and relative growth of Ashidan yak were calculated, and five nonlinear models (Logistic model, Gompertz model, Brody model, von Bertalanffy model and Richards model) were used to fit the growth curve of Ashidan yak. The fitting effect of the model was evaluated according to MSE, AIC and BIC. The results showed that the growth rate of Ashidan yak was the fastest from 12 to 18 months old, and the growth was slow or even stagnant from 6 to 12 months old. The AIC and BIC values of the Richards model were the lowest among the five models, with an AIC value of 4543.98 and a BIC value of 4563.19. The Richards model estimated body weight at 155.642 kg. In summary, the growth rate of female Ashidan yak changes with the seasons, growing faster in warm seasons and slower in cold seasons. Richards model is the best model to describe the growth curve of female Ashidan yak in five nonlinear models.

Nonlinear modeling of growth and study of a new characterization point applied to Timahdit breed sheep under uncertain paternity

The objectives of this study are to determine which model, among those of Brody, Logistic, Gompertz, Von Bertalanffy and Richards, is the most appropriate to describe the growth of this breed and to choose a point for this model which would be closest to the slaughter weight which have to be used as a selection criterion. Also, in preparation for genetic evaluation under uncertain paternity, Henderson's Average Numerator Relationship Matrix method was used and an R code was developed for the creation of the inverse matrix A which was replaced the pedigree in the animal model. To do this, 64,282 observations corresponding to 12,944 animals, collected between 2009 and 2016, were analyzed. The Von Bertalanffy function had the smallest AIC, BIC, and deviance criteria values, indicating better data representation for both sexes. Considering the average slaughter live weight of 29.4kg in the study area, the new point of characterization named f(tbm), later than the inflection point of the growth curve, is closer to the commercial target for females intended for regular slaughter supplying butchers and for animals of both sexes intended for religious festivals. Therefore, it would be appropriate to consider this point as a selection criterion for this breed. The developed R code will be integrated into a free R package allowing the estimation of the genetic parameters of the traits of the Von Bertalanffy model.

Associations of emotional social support, depressive symptoms, chronic stress, and anxiety with hard cardiovascular disease events in the United States: the multi-ethnic study of atherosclerosis (MESA)

BackgroundCardiovascular diseases (CVDs) are a major cause of morbidity and mortality around the globe and psychosocial factors are not sufficiently understood.AimIn the current study, we aimed to evaluate the role of different psychosocial factors including depressive symptoms, chronic stress, anxiety, and emotional social support (ESS) on the incidence of hard CVD (HCVD).MethodsWe examined the association of psychosocial factors and HCVD incidence amongst 6,779 participants in the Multi-Ethnic Study of Atherosclerosis (MESA). Using physician reviewers’ adjudication of CVD events incident, depressive symptoms, chronic stress, anxiety, emotional social support scores were measured by validated scales. We used Cox proportional Hazards (PH) models with psychosocial factors in several of the following approaches: (1) Continuous; (2) categorical; and (3) spline approach. No violation of the PH was found. The model with the lowest AIC value was chosen.ResultsOver an 8.46-year median follow-up period, 370 participants experienced HCVD. There was not a statistically significant association between anxiety and HCVD (95%CI) for the highest versus the lowest category [HR = 1.51 (0.80–2.86)]. Each one point higher score for chronic stress (HR, 1.18; 95% CI, 1.08–1.29) and depressive symptoms (HR, 1.02; 95% CI, 1.01–1.03) was associated with a higher risk of HCVD in separate models. In contrary, emotional social support (HR, 0.98; 95% CI, 0.96–0.99) was linked with a lower risk of HCVD.ConclusionsHigher levels of chronic stress is associated with greater risk of incident HCVD whereas ESS has a protective association.

Mathematical Modeling to Assess the Impact of Covid-19 Transmission in Guyana, South America

Objective: This study aims to find the best mathematical model for modeling the Covid-19 data of Guyana. Methods: The 2-parameter, 3-parameter Weibull distribution, and the Transmuted Weibull Distribution was used to model the Covid-19 data of Guyana using cumulative deaths that occurred on a daily basis from March 12th, 2020 to November 30th, 2021. The Covid-19 data of Guyana was extracted from the ‘ourworldindata’ website. Findings: The transmuted Weibull distribution is the best model for modeling the Covid-19 data of Guyana since it had the lowest AIC value than the other models. Novelty: Several transmuted distributions were developed to model the Covid-19 data of France, the United Kingdom, and Canada. However, in this study, a different transmuted distribution was chosen to model the Covid-19 data of Guyana. Keywords: Mathematical Modeling; Covid19; Cumulative Deaths; Transmuted Weibull Distribution and Simulation Study

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach

BackgroundIn Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. MethodsA total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg−1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. ResultsThe mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. ConclusionThe findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.

Beyşehir Gölü Populasyonundaki Sudak (Sander Lucioperca Linnaeus, 1758) Balıklarının Vücut Uzunluğu ile Canlı Ağırlıkları Arasındaki İlişkilerin Matematiksel Modellerle İncelenmesi

The aim of this study is to examine the relationship between the total length and standard lengths with live weight of the freshwater pikeperch growing in the Beyşehir Lake population using four mathematical models. 50 female fish materials of marketable size limit age were used. The data obtained were divided into two groups: estimation (70%) and test (30%). The applied models are exponential decay, exponential and Wood models, and in such research generally preferred power model. R2, R2adj, RMSE, AIC, BIC and MAPE values were used as goodness of fit tests and comparison criteria of the models. According to the study results, the total length gives more accurate estimates than a standard length in the estimating live weight. As a result, it was concluded that other models might provide better results than the power model, and they were suggested to be used in estimating the live weight of pikeperch fish.

Time Series Analysis for The Treatment of Typhoid (Enteric) Fever in Maiduguri: Using Arima Model

This research employs the ARIMA model to conduct a thorough time series analysis on the treatment of typhoid (enteric) fever in Maiduguri. The study reveals the presence of both trend and seasonality in the data, with the trend indicating a recent reduction in the recorded data. Using autocorrelation and partial autocorrelation function (ACF and PACF), the data can also be utilized to determine the model's order. The model obtained is subjected to model diagnostics to determine its efficiency and the model is used to forecast the typhoid fever. From the forecast graph shows that there may be a decrease in future years due to the pattern of the series the impression, we obtain from the graph is that predicted series seems to be trend upward and then downward. ARIMA (1,0,0) has the minimum value of AIC therefore it found to be best model. Hence, the model to fit the typhoid fever based on diplomatic test, which is LJung Box test from the family of Box Janks procedure, then our P-value is less than 0.05 level of significant, we reject the null hypothesis and conclude that the typhoid fever is statistically significant at 5% level of significant. Forecast of typhoid fever from February to December 2025 we also conclude that the typhoid fever is stable. Improve Sanitation and Hygiene: Implement measures to improve sanitation and hygiene practices, especially in areas with high disease prevalence. This may include promoting access to clean water, proper waste management, and hygiene education campaigns.Keyword: ARIMA Model, Typhoid fever, Box janks, WHO, Model diagnostic.

PENERAPAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENCEMARAN AIR BERDASARKAN TOTAL COLIFORM DI PROVINSI JAMBI

The purpose of this study is to obtain a local estimation model formed by considering spatial factors on the level of water pollution based on total coliforms in Jambi Province and can determine the most dominant factors affecting the total coliform water of PDAM Districts / Municipalities in Jambi Province using Geographically Weighted Regression (GWR) analysis. The type of data used in this study is primary data in quantitative form. This primary data was obtained by taking PDAM water samples directly in 9 regencies/cities in Jambi Province. The results of GWR analysis show that the dominant factors affecting the total coliform of PDAM water in each district/city in Jambi Province vary with different estimated parameters. The best model formed is a model with the GWR method, the adaptive bisquare weighting function which is shown from the value of the model's goodness criteria, namely the smallest AIC value and the largest coefficient of determination

Forecasting of Average Monthly Flow for Two Stations at Khabour River Using Arima And Ann Models

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the average monthly flow time series of two stations, named Begova and Chemcermo, for the Khabour River. The analysis of the time series was performed using several criteria and tests. The autocorrelation function (ACF) and partial autocorrelation function (PACF) were applied to check the accuracy of the ARIMA model. The Akaike (AIC) and Bayesian (BIC) equations were performed to determine the optimum model for prediction, which depended on the lowest AIC and BIC values. Applied test results show that the models of order ARIMA (0,0,0)(3,0,0)12 and ARIMA((0,0,5)(5,1,4)12 have higher acceptance compared to the other models for predicting the average monthly flow for Begova and Chemecermo stations, respectively. An ANN model of type multilayer perceptron method (ANN-MLP) was used for predicting the average monthly flow of Begova and Chemecermo stations, where the best models found are MLP (5,3,1) and MLP (9,7,1), respectively. Different statistical tests were applied and showed that the efficiency of the ANN model was better than the ARIMA model, with deterministic coefficients of 0.914 and 0.876 compared to 0.854, and 0.852 for the ARIMA for the monthly time series of Begova and Chemecermo stations, respectively

Forecasting of Average Monthly Flow for Two Stations at Khabour River Using Arima And Ann Models

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the average monthly flow time series of two stations, named Begova and Chemcermo, for the Khabour River. The analysis of the time series was performed using several criteria and tests. The autocorrelation function (ACF) and partial autocorrelation function (PACF) were applied to check the accuracy of the ARIMA model. The Akaike (AIC) and Bayesian (BIC) equations were performed to determine the optimum model for prediction, which depended on the lowest AIC and BIC values. Applied test results show that the models of order ARIMA (0,0,0)(3,0,0)12 and ARIMA((0,0,5)(5,1,4)12 have higher acceptance compared to the other models for predicting the average monthly flow for Begova and Chemecermo stations, respectively. An ANN model of type multilayer perceptron method (ANN-MLP) was used for predicting the average monthly flow of Begova and Chemecermo stations, where the best models found are MLP (5,3,1) and MLP (9,7,1), respectively. Different statistical tests were applied and showed that the efficiency of the ANN model was better than the ARIMA model, with deterministic coefficients of 0.914 and 0.876 compared to 0.854, and 0.852 for the ARIMA for the monthly time series of Begova and Chemecermo stations, respectively

A STUDY OF SMALL AREA ESTIMATION TO MEASURE MULTIDIMENSIONAL POVERTY WITH MIXED MODEL POISSON, ZIP, AND ZINB

The research began with calculating the value of multidimensional poverty at the district level in West Java Province from SUSENAS 2021. The calculation of multidimensional poverty was based on individuals in each district or city household. The dimensional weights are weighed the same, and the indicators in the dimensions are also weighed the same. Furthermore, the simulation study used the Poisson, ZIP, and ZINB mixed models to examine the model's performance on data with cases of excess zero values and overdispersion. The simulation was by generating data without overdispersion and with overdispersion. Overdispersion data was generated with parameters of ω (0.1, 0.3, 0.5, and 0.7), and the model was evaluated from the AIC value. The best method in the simulation study was used to estimate multidimensional poverty in sub-districts in West Java Province using PODES 2021. Simulation studies on data without overdispersion showed no difference in the model's goodness. Overdispersion data shows Mixed Model ZIP and ZINB are better than Mixed Model Poisson. The percentage of the multidimensional poverty population at the sub-district level in West Java Province is quite diverse, from 0.04% to 75.54%.

OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION

Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value. A negative binomial regression can overcome an unfulfilled equidispersion assumption where the mean is greater than the standard deviation value (overdispersion). This method is more flexible because it does not require that the variance be equal to the mean. The case studies used in this research are cases of anemia in women of childbearing age (WCA) in 33 provinces of Indonesia. This study aims to apply the Poisson regression method and negative binomial in the case data of anemia in WCA to prove the model's goodness and find the factors that influence anemia in WCA. This data was obtained from biomedical sample data for Riset Kesehatan Dasar (Riskesdas) and data obtained from the website of the Badan Pusat Statistik (BPS) in 2013. By applying these two methods, the result is that negative binomial regression is the best model in modeling WCA cases with anemia in Indonesia because it has the smallest AIC value of 221.72; however, the difference is not too far from the AIC in the Poisson regression model, which is 221.83. It can also be supported that Poisson regression is unsuitable for the analysis because of the case of overdispersion. With a significance level of 10%, the number of WCA affected by malaria per 100 population influences cases of WCA anemia. At the same time, other independent variables have no effect.

A Comparative Approach on Bridge and Elastic Net Regressions

Machine Learning techniques such as Regression have been developed to investigate associations between risk factor and disease in multivariable analysis. However, multicollinearity amongst explanatory variables becomes a problem which makes interpretation more difficult and degrade the predictability of the model. This study compared Bridge and Elastic Net regressions in handling multicollinearity in multivariable analysis. Wisconsin Diagnostic Breast Cancer data was used for comparison for model fit and in handling multicollinearity between the regression techniques. Comparison were made using MSE, RMSE, R^2, VIF, AIC and BIC for efficiency. Scatter plots was employed to show fitted regression models. The results from the study show that, the Bridge regression performed better in solving the problem of multicollinearity with VIF value of 1.182296 when 𝛾 = 2 compared to Elastic Net regression with a VIF value of 1.204298 respectively. In comparison for best model fit, Bridge regression with 𝛾 = 0.5 performed better with MSE of 11.58667, AIC value of 258.9855 and BIC of 277.2217 respectively. Consequently, we can conclude that both the Bridge and Elastic Net Regressions can be used in handling multicollinearity problems that exist in multivariable regression analysis. Information on machine learning such as this, can help those in the medical fields to improve diagnosis, narrow clinical trials and biopsy to proffer effective treatment.

Application of Poisson and negative binomials models to estimate the frequency of insurance claims

Generalized Linear Models (GLMs) are a modeling approach that allows the modeling of nonlinear behaviors and non-Gaussian distributions of residues. This approach is very useful for general insurance analysis, where the frequency of claims and the amount of claims distributions are usually non-Gaussian. In this article, the application of Poisson and Negative Binomial models to estimate the frequency of claims of auto insurance is discussed. The accuracy of the models was compared to choose the best model for determining pure insurance premiums using R software. The data used are a secondary dataset which is the motor vehicle insurance dataset from Sweden named dataOhlsson and the motor vehicle dataset from Australia named ausprivauto0405. The results of the exploration of the GLMs model are that Poisson's GLM and Negative Binomial models both are suitable models for estimating the number of claims for the dataOhlsson dataset. Both models have relatively similar parameter estimates, as well as the AIC and BIC values for the dataOhlsson dataset, however, both models are not suitable for estimating the number of claims for the ausprivauto0405 dataset. More investigation using different models is needed to ensure which model is more appropriate for estimating the frequency of insurance claims.

Assessing the Best Fit Volume and Carbon Stock Equation of Pinus Roxburghii with Respect to Diameter at Breast Height Using Non-Destructive Method

Pinus roxburghii is dominant timber species in Nepal. However, there is not any model showing correlation of Diameter at breast height (DBH) with Volume and Carbon Stock. Therefore, this study was objectively conducted to show the correlation of diameter with volume and carbon stock. Gaumati community forest was selected as study site for this research. Total 214 sample trees were taken into account for the study. DBH classes were categorized into s 0-10, 11-20, 21-30, and 31-40, 41-50, and DBH > 50 cm to represent the most of the diameter classes. Height and DBH were measured using Clinometer and Diameter tape. Four types of models were developed for volume and carbon stock with DBH viz. linear, logarithmic, power, and exponential. The models were tested, out of which the power equation was found to be the most accurate for both carbon and volume calculations. Power volume and carbon stock equations are y = 0.00004 x2.8404 (y=volume, x=DBH) and y = 0.0125x2.8404 (y=carbon stock, x=DBH) respectively. The estimated values of R2, MAD, MSE, RMSE and AIC were 0.9817, 0.178, 0.093, 0.305 and -41.58 respectively for power volume model and these values of R2, MAD, MSE, RMSE and AIC were 0.9817, 21.92, 1379.01, 37.13 and 1194.18 respectively for carbon stock calculation. Study shows that, there is a strong correlation between DBH vs. volume and DBH vs. carbon stock. Thus, the volume equation and carbon stock equation will be useful for forest science to calculate the volume and carbon stock of standing plants.

Non-stationary time series data for natural rubber inventory forecasting: A case study

PPLK Corp. is a company that uses natural rubber as the main raw material to produce crumb rubber. The problem identified in PPLK Corp. is the insufficient amount of natural rubber received to produce and fulfill consumer demand. There have been fluctuations in the amount of natural rubber received and high variability between periods. To minimize this variability, it is necessary to forecast natural rubber requirements. The purpose of this study is to forecast the natural rubber inventory for the next periods using the best-fitted model, which is the Autoregressive Integrated Moving Average (ARIMA) method. A total of 547 daily data points from 2021 to 2022 were used. As a result, the ARIMA (1,1,2) model was found to be the best model for natural rubber forecasting in the rubber factory. The ARIMA (1,1,2) model had the smallest AIC value compared to others. The total daily natural rubber need is forecasted to be around 67.588 kilograms with a range between 64.805 and 70.421 kilograms per day. However, it should be noted that this study was limited to short-term forecasting only.

Model Comparison of Covid-19 in Lampung Indonesia

The COVID-19 has been pandemic in the world and has resulted in so many deaths due to being infected by the virus, therefore this study aims to determine a suitable model in estimating the number of deaths due to being infected by the COVID-19. This study focus in Lampung, Indonesia. The analysis of the death number was using three methods, there waspoisson regression, negative binomial regression (NBR), and generalized poisson regression (GPR). From the results, three predictor variables have significant effect to the model, there was positive cases of COVID-19, number of poor people, and life expectancy, while population density per km2 has no significant effect. The best estimation model has smallest AIC and BIC values, and the poisson regression method is the best among other methods.
 Keywords: Poisson Regression, NBR, GPR, COVID-19

Model Exponential GARCH (EGARCH) Untuk Memprediksi Harga Saham PT Merdeka Copper Gold Tbk

The price of gold is determined by trading in the gold market and its derivatives. This study aims to determine the EGARCH model in predicting gold stock prices at PT Merdeka Copper Gold Tbk and to obtain the prediction results of the EGARCH model at PT Merdeka Copper Gold Tbk. There are two asymmetric GARCH response modeling techniques, namely the Threshold GARCH model (TGARCH) from Glosten, et al (1993) and Exponential GARCH (EGARCH) from Nelson (1991). The research results show that the ARIMA model (0,1,1) has the smallest AIC value of 6096.38. The GARCH model is obtained by GARCH (0,1), so it can be modeled using the EGARCH model. The EGARCH model obtained is EGARCH (1.7).