AIC Value Research Articles

Peramalan Jumlah Titik Panas Provinsi Kalimantan Timur Menggunakan Analisis Intervensi Fungsi Pulse

Intervention analysis is a time series analysis that used to explain the influence of intervention caused by external and internal factors. As for the number of hotspot in East Borneo which was increased in 2015. The purpose of this study was to determine the best intervention model for forecasting the number of hotspots in East Borneo. In the initial stage of the intervention analysis is to divide the data into 2 parts, namely data before the intervention and data after the intervention occurred. The results of the analysis obtained the best model for the data before the intervention occurred were SARIMA (0,1,1)(0,1,1)12. The next step was identifying the intervention function by observing the residual graph of the SARIMA model and obtained the order b = 0, s = 0 and r = 0 with the AIC value of the intervention model of -143,16. Furthermore, based on the intervention model obtained forecasting results is increased from July to September 2019. The number of hotspots with the highest number of hotspots occurring on September 2019 with 249 hotspots. Then decreasing on October 2019 to 183 hotspots. On November 2019 it dropped significantly to 13 hotspots.

Modeling the Count Data of Public Health Service Visits with Overdispersion Problem by Using Negative Binomial Regression

The count data of health service visits can be modeled into Poisson regression analysis, where there is no overdispersion assumption by looking at the comparison between mean and variance. The overdispersion test is performed by using the ratio of the sum of Pearson residuals over the number of degrees of freedom that must be less than one. The overdispersion problem can be corrected accurately by building mixture distribution where the parameter of Poisson distribution is made to have Negative Binomial distribution as the theoretical model. The data used in this study are the number of visits to public health service at Padang city as many as 460 data, where the predictor variables are age, gender, education level, occupation, income, home health status, individual health status, health insurance type, distance to health service, and diet type. The best model of negative binomial regression is selected by considering the values of AIC, BIC, Log-likelihood, and overdispersion tests that occur between the resulting models. The final result of this count data model with negative binomial regression fits better and overcomes the overdispersion problem with the significant variable is individual health status for this population, and it can be explained that the more individual has a history of having severe illness the more often the number of visits to the health service, meanwhile the other predictor variables have no effect to the number of visits.

Pemodelan Generalized Additive Model For Location, Scale, and Shape (Gamlss) Dengan Pemulusan Locally Estimated Scatterplot Smoothing (Loess) pada Kasus Hiv/Aids Di Jawa Timur

HIV / AIDS is a contagious disease that can attack all age groups of the population and is a health challenge in almost all over the world including Indonesia. Therefore, it is necessary to model HIV / AIDS cases for the factors that are suspected to influence them. One suitable method for estimating factors that influence HIV / AIDS is the Generalized Additive Model for Location, Scale, and Shape (GAMLSS). The GAMLSS method is flexible because it includes expansion of a good exponential family distribution to handle overdispersion data, continuous data, and discrete data. This research will apply GAMLSS semiparametric modeling with LOESS smoothing to find out the characteristics and models of HIV / AIDS cases in East Java in 2017. Based on the analysis, it was found that the variables that significantly affected were the number of homeless people, number of victims of drug abuse, population poor, and the number of fertile age couples using condom contraception with AIC value of 437,404, degree = 1 and span = 0.3, and the distribution used is Negative Binomial I.

PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC

Can Generalized Poisson model replace any other count data models? An evaluation

BackgroundCount data represents the number of occurrences of an event within a fixed period of time. In count data modelling, overdispersion is inevitable. Sometimes, this overdispersion may not be just due to the excess zeros but may be due to the presence of two or more mixtures. Hence the main objective is to examine for the presence of mixtures if any, with excess zeros and compare Generalized Poisson model, Mixture models with other count data models using real time and simulated data. MethodsThree real time over-dispersed datasets were used for the comparison of the models. The real time data models were compared using information criteria like AIC and BIC and regression coefficients. Data was also simulated using mixture Poisson with excess zeros. The simulation was repeated for different sample sizes were used to identify the better model. ResultsGeneralized Poisson showed consistently lower bias and MSE when compared to the other models for varying sample of sizes. AIC and BIC values were almost similar for Generalized Poisson, ZIP and Mixture Poisson model. Similar findings were also obtained from real time data. ConclusionGeneralized Poisson models provides a better fit for overdispersed data due to excess zeros, consistently in real time and simulated with varying sample sizes. Negative Binomial models can be redistricted or re-evaluated against Generalized Poisson model.

MEMODELKAN ANGKA GIZI BURUK DI PROVINSI BALI DENGAN PENDEKATAN REGRESI SPASIAL

Nutritional problems in toddler are still a serious problem in various districts/cities in Indonesia. The case of malnutrition in Bali Province vary in many regions and hypothesized to be influenced by geographic location, which is often known as spatial heterogeneity. To overcome this problem, a spatial regression method is used on this research. This study aims to model the factors that are hypothesized affect malnourished toddlers in Bali Province using spatial regression methods, i.e. spatial autoregressive model (SAR) and spatial error model (SEM). Both models have 5 predictors variable, i.e. the percentage of toddlers aged between 6 - 59 months who received vitamin A, the percentage of babies with low birth weight (LBW), the percentage of households with clean and healthy living behavior (PHBS), the percentage of children under five receiving exclusive breastfeeding, and the percentage of toddler health services, which are obtained from Bali Provincial Health Office. The results showed SEM method produced smaller AIC value and higher , with and AIC values ??of 96.24% and 60.84, respectively.

Comparison of the Performance of the SANN, SARIMA and ARIMA Models for Forecasting Quarterly GDP of Nigeria

This research aimed at modelling and forecasting the quarterly GDP of Nigeria using the Seasonal Artificial Neural Network (SANN), SARIMA and Box-Jenkins models as well as comparing their predictive performance. The three models mentioned earlier were successfully fitted to the data set. Tentative architecture for the SANN was suggested by varying the number of neurons in the hidden layer while that of the input and output layer remained constant at 4. It was observed that the best architecture was when the hidden layer had 10 neurons and thus SANN (4-10-4) was chosen as the best. In fitting the ARIMA/SARIMA models, the Augmented Dickey Fuller (ADF) test was used to check for stationarity. Variance stabilization and Stationarity were achieved after logarithm transformation and first regular differencing. The ARIMA/SARIMA model with lowest AIC, BIC and HQIC values was chosen as the best amongst the competing models and fitted to the data. The adequacy of the fitted models was confirmed observing the correlogram of the residuals and the Ljung-Box Chi-Squared test result. The SANN model performed better than the SARIMA and ARIMA models as it had a Mean Squared Error value of 0.004 while SARIMA and ARIMA had mean squared errors of 0.527 and 0.705 respectively. It was concluded that the SANN which is a non-linear model be used in modelling the quarterly GDP of Nigeria. Hybrid models which combine the strength of individual models are recommended for further research.

Psychometric properties of the Persian translation of maternal postpartum quality of life questionnaire (MAPP-QOL)

Background and objectiveMany studies have been conducted in Iran on the postpartum quality of life, and the majority have used the general quality of life questionnaire. With a specific tool in this context, the dimensions of maternal postpartum quality of life can be more accurately determined. The present study was conducted to determine psychometric properties and validate the Persian version of the Maternal Postpartum Quality of Life Questionnaire (MAPP-QOL).Materials and methodsThe present methodological study was conducted in 2018. The original version of MAPP-QOL was translated into Persian by both forward and backward translation. In a cross-sectional study, the Persian version was completed by 407 eligible postpartum women aged 18 to 47 and living in Tehran. Reliability of the questionnaire was assessed using Cronbach's alpha coefficient and test–retest. For construct validity, exploratory and confirmatory factor analyses were used.ResultsThe MAPP-QOL showed good content validity; content validity ratio (CVR) ranged from 0.6 to 1.00, and content validity index (CVI) ranged from 0.7 to 1.00. Using exploratory factor analysis, five factors, including Socioeconomic; Relational/Family-Friends; Psychological/Baby; Health & functioning; and Relational/Spouse-Partner, were extracted, which together explained 78.84% of the total variance. After modifications of CFA, the confirmatory factor analysis showed an acceptable goodness-of-fit. AVE value Above 0.5 exhibited appropriate convergent validity, and AVE greater than MSV confirmed divergent validity. The Cronbach's alpha, McDonald's Omega, Composite reliability and maximum reliability H of the five extracted factors were excellent (> 0.9). Also, the AIC values of the factors were good (between 0.721 to 0.859).ConclusionThe 38-item Persian version of the postpartum quality of life questionnaire is adequately reliable for postpartum women in Iran. Given its appropriate psychometric properties, this scale is fit to be used in future studies on postpartum women.

Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models

This study tried to demonstrate the role of time series models in modeling and forecasting process using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box–Jenkins methodology, the ARIMA(1,1,1)(2,0,0)12 with drift model was selected to be the best fit model for the time series, according to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 9 neurons in the hidden layer and 6 time delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The Mixed model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities can be a better choice for modelling the time series. The comparative results revealed that the Mixed-LM model with 9 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 9 neurons in the hidden layer and 6 time delays, and the ARIMA(1,1,1)(2,0,0)12 with drift model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating local mean sea level forecast in advance. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.

Spatial Econometric Model on Economic Growth in West Nusa Tenggara

Gross Regional Domestic Product (GRDP) is a reflection of a region's economic growth. West Nusa Tenggara (NTB) is one of the provinces that contributes to good GRDP for Indonesia. The purpose of this research is to modeling GRDP in NTB using spatial econmetrics. The data used is the GRDP data of each district / city in NTB Province as a response variable and factors that affect the number of workers, capital value and electrification ratio as predictor variables. The results showed that there is a spatial dependence on the district / city GRDP in NTB Province on the error model so that the model formed is the Spatial Error Model (SEM) with a rho of 71.1% and an AIC value of 173.34.

Vehicle Road Accident Prediction Model Along Federal Road FT050 Kluang-A/Hitam-B/Pahat Route Using Excess Zero Data


 
 
 Traffic accidents have become a major socio-economic problem in Malaysia and are considered the primary cause of mortality. Over 60 percent of these fatal accidents occurred on rural roads. Nearly half of all fatalities took place on federal roads and over a quarter happened on state roads. It is also estimated that about 2 percent of the country's Gross Domestic Product (GDP), or approximately RM 9 billion, is lost through road accidents. Several models therefore have been developed for modelling the occurrence of accidents, but most of these models have plenty of deficiencies. The following study focuses on stochastic regression models, such as Poisson, Negative Binomial, Zero- Inflated Poisson and Zero-Inflated Negative Binomial with excess zero outcomes on the response variables. Furthermore, R-statistical programming is used to specify the regression relationship with a sophisticated result. The method used consider as updating approach in predicting potential road accidents, which can also produce an accuracy probability of hazardous locations. Based on road accident data collected over a five-year period from 2010 to 2014 at Federal Road F0050: Kluang- A/Hitam-B/Pahat in Johor, Malaysia, results of this study show that Zero Inflated model performed better, in terms of the comparative criteria based on the AIC value.
 
 

OCEANOGRAPHIC VARIABILITY ANALYSIS USING GIS AND GAM FOR POTENTIAL FISHING ZONE MAPPING OF BIG EYE TUNA (Thunnus obesus)

Southern Waters of Java Island are 573 fisheries management zones, the Big Eye Tuna ( Thunnus obesus ) commodity is one of fish that has high economic value. There are oceanographic factors affect fish distribution of Big Eye Tuna ( Thunnus obesus ) such as sea surface temperature, chlorophyll-a, and depth. The data research of Big Eye Tuna fishing zone was obtained from PPSC with data description were year, month, day, number of trips, and number of fish caught around 2018-2019. Oceanographic data used from citra satellite Aqua MODIS for sea surface temperature and chlorophyll-a, and citra satellite ETOPO-1 for depth, by using GAM statistical analysis by looking at the lowest AIC value and the largest CDE value of 7 combined models of oceanographic parameters. The results obtained from statistical analysis using the GAM which was very influential in determining the potential zone for the fishing of Big Eye Tuna ( Thunnus obesus ) showed that a combination of sea surface temperature + chlorophyll - a + depth with an AIC value of 28840.02 and CDE of 11.1%, with the results Oceanographic parameters optimum sea surface temperature was 25 ° C- 30 ° C, for chlorophyll - a 0.08 - 0.30 mg / m3, and for an average depth of 3500m. The depth parameter had a less positive influence because the Cilacap fishermen used FADs for the fishing area of Big Eye Tuna. The distribution area of Big Eye Tuna ( Thunnus obesus ) in the Southern of Java Island is at latitude (-7.3 °) - (-14.83 °) and longitude 102 ° - 115.25 °.

High performance approach for water level forecasting in Yom River basin of Thailand

Analyses of average monthly water level (AMWL) time series (April 2007 - March 2020) at four water level measurement stations (Y.31, Y.20, Y.1C, Y.37) for wet and dry seasons in the Yom River basin of Thailand. Using Box-Jenkins method, eight best-fit seasonal ARIMA models for one hydrological year forecasting for wet and dry seasons of AMWL were selected; among from twenty-four possible models, by minimum values of AIC, SBIC RMSE, and MAPE. Besides, The comparisons with two benchmark models, Holt-Winters’ Seasonal additive method, and Seasonal Naive method were applied. Results indicated that: The four selected SARIMA models for wet seasons of Y.31, Y20, Y1C, and Y.37 are SARIMA(1,1,1)(1,0,0)[6], SARIMA(1,1,1)(1,0,0)[6], SARIMA(1,1,2)(1,0,0)[6], and SARIMA(1,1,1)(1,0,0)[6], respectively. While the models for dry seasons are SARIMA(0,1,1)(2,0,0)[6], SARIMA(1,1,1)(1,0,1)[6], SARIMA(1,1,1)(2,0,0)[6], and SARIMA(1,0,0)(1,0,1)[6]. The forecasting performance are the minimum values of RMSE and MAPE between SARIMA and the benchmark models. The SARIMA model is the best approach for Y.31 Station [Wet Season], Y.31 Station [Dry Season], and Y.37 Station [Dry Season], while the best method for Y.20 Station [Wet Season], Y.1C Station [Wet Season], Y.37 Station [Wet Season], Y.20 Station [Dry Season], and Y.1C Station [Dry Season] is Holt-Winters’ seasonal additive method. The upstream station (Y.31 station) has higher accuracy than the downstream station (Y.37 station) due to human activities that disturb hydrological changes. Furthermore, the dry season forecasting is more accurate than the wet season.

Parametrik hızlandırılmış başarısızlık (ölüm) zamanı (HBZ) modelleri ile erken evre meme kanseri hastalarında uygulama

Purpose: Cox Proportional Hazard (CPH) model is the most commonly used multivariate regression model for survival analysis. However, it is not always possible to obtain the proportional hazard (PH) assumption. In this case, Parametric Accelerated Failure Time (AFT) models may be applied. In this study, AFT and CPH models were applied to patients with early stage breast cancer and the results were compared. Materials and Methods: Retrospective survival data of 697 patients with early stage breast cancer were analyzed in this study. 13 independent variables and overall survival time as the dependent variable were tested. Multiple CPH regression analysis were performed for significant variables . For the AFT model, hazard functions of the data belonging to log-normal, log-logistic, Weibull and of exponential distributions were examined. Results: Although age groups, tumor grade, neural invasion and extra capsule involvement did not provide the assumption of COH, when statistically significant 9 independent variables were applied to the multivariate COX model, metastatic lymph nodes and menopausal status were found to be significant. According to AIC value and hazard function distributions, the most appropriate AFT model was with log-logistic. AFT model with log-logistic regression, number of metastatic lymph nodes, menopausal status and tumor size were found to be significant. Conclusion: In the literature, the CPH model is one of the most commonly used survival models. In cases where the assumption of the proportionality of hazards were violated; it may be more appropriate to use alternative models such as the AFT regression model.

Constraint and Unconstraint of Vector Autoregressive Model; Using GDP Growth Rate of Agriculture, Industries, Building/Construction, Whole-Sale/Retail and Services in Nigeria

In this research, multivariate Time Series was adopted to model the Gross Domestics Product (GDP) growth rate of Nigeria on five (5) variables namely: Agriculture, Industries, Building/construction, Wholesales/Retails trade and Services The data was collected from National Bureau of Statistics, range quarterly from 1985 to 2017, a total of 33years. Real (R) software was used as a tool to analyze the model. The data were grouped into 10 pairs of 2 parameter variables, 10 pairs of 3 parameters variables, 5 pairs of 4 parameters variables, and the complete 5 parameter variables. In each group, the best model was selected and Lag's using Akaike Information Criteria, then the unconstrained (vector autoregressive) AIC of the model was compared with that of constrained (simplified vector autoregressive) AIC model. The unconstraint models with AIC values (-11.973, -17.1111, -22.1823, and 25.8996) at lag (5) was compared with that of constraint models with AIC values of (-12.5116, -17.5298, -22.2894 and -25.9916), the outcome showed that constraint models performed better than unconstraint models.

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

This study is designed to model and forecast Nigeria’s stock market using the All-Share Index (ASI) as a proxy. By employing the Markov regime-switching autoregressive (MS-AR) model with data from April 2005 to September 2019, the study analyzes the stock market volatility in three distinct regimes (accumulation or distribution – regime 1; big-move – regime 2; and excess or panic phases – regime 3) of the bull and bear periods. Six MS-AR candidate models are estimated and based on the minimum AIC value, MS(3)-AR(2) is returned as the optimal model among the six candidate models. The MS(3)-AR(2) analysis provides evidence of regime-switching behaviour in the stock market. The study also shows that only extreme events can switch the ASI returns from regime 1 to regime 2 and to regime 3, or vice versa. It further specifies an average duration period of 9, 3 and 4 weeks for the accumulation/distribution, big-move and excess/panic regimes respectively which is an evidence of favorable market for investors to trade. Based on Root Mean Square Error and Mean Absolute Error, the fitted MS-AR model is adjudged the most appropriate ASI returns forecasting model. The study recommends investments in stock across the regimes that are switching between accumulation/distribution and big-move phases for promising returns.

Modeling and predicting the growth of indigenous Harnai sheep in Pakistan: non-linear functions and MARS algorithm

Five non-linear functions, i.e. Gompertz, Logistic, Negative exponential, Brody and Bertalanffy, and multivariate adaptive regression splines (MARS) data mining algorithm were implemented with the objective to describe the body weight-age relationship of Harnai sheep of Balochistan, Pakistan. The data comprised of 1317 records of body weight from birth to 1 year were provided from Multi-Purpose Sheep Research Station Loralai, Balochistan. Each non-linear function and MARS algorithm were fitted to the data of male and female, single and twin and all lambs. Comparison among different non-linear models was based using the adjusted coefficient of determination ([Formula: see text]), Durbin-Watson statistic (DW), root mean square error (RMSE), Akaike's and Bayesian information criteria (AIC and BIC) and the coefficient of correlation (r) between observed and fitted live body weight. The best fit was provided by the Brody model in terms of the highest [Formula: see text] and r values and lowest RMSE, AIC and BIC values in male and female, single and twin and all lambs followed by Bertalanffy, Gompertz, Negative exponential and Logistic model in order of their goodness. The negative correlation between asymptotic weight and maturing rate inferred that animals with smaller mature weight mature fast. Though males and singles were found heavier at mature weight than females and twins, respectively, they mature more slowly. The results of the study suggested the use of the Brody model to accurately describe the weight-age relationship of Harnai sheep. The present study also showed a very high predictive performance of the MARS data mining algorithm for describing the growth of sheep. In conclusion, MARS algorithm may be a good alternative for breeders aiming at describing the weight-age relationship of Harnai sheep.

Selection of variables in generalized linear mixed model for smoker in Jambi Province

Smoking is one of the health problems in Indonesia. Many factors cause a person to smoke, both originating from oneself and the environment. The statistical question that arises is how to choose the factors that are most significant in influencing people to smoke. These factors are the variables that will sed in modeling. This study aims to select the variables in the compressed linear mixed model using the Lasso penalty and the Boosting function, using the EM and REML algorithms. Respondents in this study were 160 smokers in Jambi Province. Based on the AIC value, the best model obtained from the selection of variables with the Boosting function and REML algorithm. The analysis shows that work, welfare level, and family members who smoke are the factors that influence people smoking in Jambi Province.

The Model of Per-Capita Expenditure Figures in Sumatera Selatan uses a Geographically Weighted Panel Regression

The Geographically Weighted Panel Regression (GWPR) is a development of a global regression model where the basic idea is taken from a combination of panel data and GWR. The GWPR model is built from the point approach method, which is based on the position of the coordinates of latitude and longitude. The parameters for the regression model at each location will produce different values. GWPR can accommodate spatial effects, so that it can better explain the relationship between response variables and predictors. The purpose of this study is to compare the GWPR model with the Fixed Gaussian and Adaptive Bisquare weighting functions based on the AIC value. The data used in this study is secondary data taken from the website of the Central Statistics Agency (BPS) in the form of Per-Capita Expenditure Figures in South Sumatra in 2013-2019. This research results that in the case of the Per-Capita Expenditure Rate (AP), it is better to use the GWPR method with a fixed gaussian weighting function in the modeling, where the resulting coefficient of determination is 95.81% rather than adaptive bisquare with a determination coefficient of 93.3%. The factors that influence the Per-Capita Expenditure Rate (AP) in South Sumatra on the fixed gaussian weighting are divided into 6 groups, while the adaptive bisquare is divided into 2 groups.

Penerapan Model Geographically Dan Temporally Weighted Regression Pada Kecelakaan Lalu Lintas

Geographically and temporally weighted regression (GTWR) is a model that is used to deal with instability in data both spatially and temporally and to produce local parameters. In this paper, The GTWR model is used to analyze the factors that are thought to significantly influence the number of traffic accidents in Mempawah Regency. The data used in this study came from 8 districts with the variables used were the number of traffic accidents, the number of population (gender ratio, length of damaged road conditions, and percentage of adolescence. The parameter estimation of the GTWR model was obtained using the weighted least square (WLS) method. The optimal bandwidth selection uses the Cross-Validation (CV) method and the weighting used is the Fixed bisquare function. The results of the analysis show that using the GTWR model, it was found that only the population size variable significantly affected the number of traffic accidents in all locations in Mempawah Regency from 2015 to 2018. The GTWR model was known to be better than the multiple regression model because it produced smaller AIC and RSS values and a larger R-square value.