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Variable selection is essential in linear regression analysis to improve predictability and select significant variables. Estimating the regression coefficient on high-dimensional data cannot be done using the least squares method, so it requires specific analytical techniques. Approaches that can take on high-dimensional data include SCAD, LASSO, and Elastic Net. This research will analyze the most crucial method between SCAD, LASSO, and Elastic Net on Low Birth Weight (LBW) data in East Nusa Tenggara (NTT). Two methods are used in this study, first, comparing the SCAD, LASSO, and Elastic Net methods using simulation data, and second, applying the logistic regression method to actual data. The data used in this study is the LBW data by fertile women in NTT from the 2017 IDHS (Indonesian Demographic and Health Survey) data. The analysis shows that the results obtained through simulation and data reveal, based on the value of the AIC model goodness test, the SCAD is better than the other methods with the smallest AIC value of 17. 58878, smaller than the AIC LASSO value of 17.90169 and Elnet of 17.88728. [ABSTRACT FROM AUTHOR]

One of the essential aspects of modeling is the model's simplicity. Several methods can simplify the model, including Ridge regression, LASSO, and Elastic Net. We developed an algorithm for selecting the variables in these models, namely Naïve and Covariance. Previous research revealed that the Covariance algorithm is superior in terms of time compared to the Naïve algorithm. It is evaluated by applying models and algorithms to male sex behavior data with the criteria of goodness, namely the simplicity of the model and the minimum AIC value. Based on the study's results, it found that the Covariance algorithm still outperformed the Naïve algorithm in all three models. [ABSTRACT FROM AUTHOR]

The generalized Mixed Linear Model is applicable in Indonesia's Low Birth Weight (LBW) case. During 2003-2028, this case happened 2.6% to 8.9% of total birth. The lowest low birth weight case was in Jambi province, and the highest was in Central Sulawesi. The forms of LBW models include census blocks as random influences, and the fixed effects used are parents' background, household socio-economic conditions, maternal conditions. The link function used was logit because the response is binary. Therefore, this research aims to estimate the GLMM of each province and then do the selection using the glmmLasso model by L1-norm. The results indicate that the results of the selection method using the glmmLasso method are simpler than the GLMM for both Jambi and Central Sulawesi provinces. This result is supported by the goodness of fit (BIC and AIC), where the BIC for glmmLasso for Jambi and Central Sulawesi provinces are 135.488 and 214.783, respectively. Equivalent to the goodness of the model, estimation of the random effect influences for the glmmLasso model is also smaller than GLMM. The variable with a significant effect on LBW in Jambi province is the mother's age, and two variables are shrunk toward zero. However, the glmmLasso LWB's model of Central Sulawesi has four variables that have a significant effect. Those variables are the father's occupation, mother's education level, level of the wealth index, and multiple births, and just one variable is reduced. [ABSTRACT FROM AUTHOR]

Indonesia is currently facing the Covid-19 pandemic, which all countries in the world have also faced in the past year. The contribution of daily cases from each province is the key to the high and low number of positive cases of Covid- 19 in.Indonesia. Based on data from the Covid-19 handling task force for Indonesia, it was noted that Jakarta is the province with the highest spread of Covid-19 cases in Indonesia. Daily positive cases in Jakarta contributed 25% to national cases. For this reason, this study aims to analyze the incidence of positive cases of Covid-19 in.Jakarta through a prediction model for time series data. The multi-input transfer function model used is one of the multivariate time series models to predict Covid-19 cases in Jakarta based on several external variables. The external variables or input series used are the Covid-19 cases in Bodetabek, close contact data, and death case data. All variables used are time-series data from April 2020 to April 2021. The analysis results show that the three input variables significantly correlate in predicting Covid-19 cases in Jakarta. The multi-input transfer function model that has been formed has a pretty good performance in predicting Covid-19 cases in Jakarta with a MAPE of 10%. [ABSTRACT FROM AUTHOR]

Growth curves are widely used in modeling the growth process. These curves can capture the pattern of the growth process to explain the characteristic of the growth process. In this research, growth curves are modelled to time series form models in the growth rate form by allowing lagged in the model. These growth time series models are applied in weekly new cases of positive COVID-19 in.Jakarta to forecast the number of new cases in the five weeks ahead in the testing data. Three schemes are made to be modelled. The growth curves that employed in this research are Logistic and Richard growth curves. Evaluations value of the Richard and Logistic models are evaluated by RMSEP of the forecasting result in testing data and RMSE of the prediction in training data to determine which model is better to fit in the weekly new cases of positive COVID-19 in.Jakarta. Time series model based on Richard growth curve seems to have better performance than Logistic growth curve in forecasting weekly new cases of COVID-19 in.Jakarta, because Richard time series models have smaller RMSE and RMSEP in almost all scheme than Logistic models. Other than that more testing data fall into the 95% confidence interval of weekly new cases in Richard time series model than Logistic time series model in all scheme. [ABSTRACT FROM AUTHOR]

Mixed model is a model that combines fixed factors and random factors while fixed model is a model that only contains fixed factors. Observations made over time with the same object being observed are called repeated measurement. This research was conducted to determine the determinant factors of internet data quota sales which are influenced by SA (Sales Area), MC (Mutual Check), PC (Product Category), and time factors using a nested linear mixed model with repeated measurement and fixed model with repeated measurement. SA, PC, and time factors as fixed factors while the MC factor nested in SA as a random factor. The results showed that in nested linear mixed model with repeated measurement, the interaction effect between three fixed factors, namely between SA, PC, and time have a significant effect on the sales volume of internet data quota. In fixed model the analysis used the average value of internet data quota sales for each MC, so there is no interaction effect between three fixed factors in the fixed model. This shows that the fixed model is simpler than the mixed model. The nested mixed model with repeated measurement can better explain the effect of MC in SA because it includes random factors, namely the MC factor nested in SA. The fixed model with repeated measurement can better explain the effect of SA because SA is a fixed factor which is the average of MC. [ABSTRACT FROM AUTHOR]

One of the problems faced by the Province of West Java is poverty. The response data used in this study is the percentage of the poor who have a non-normal distribution. Fixed effects variables consist of life expectancy, school year expectancy, and school enrollment rates aged 16-18 years. Years and clusters variables will be included as independent variables but are random effects. Therefore, for this data case, the appropriate model to be developed is Binomial GLMM and Beta-Binomial HGLM. The modeling performed on the percentage of poor population data gives relatively the same results between the model approaches using Binomial GLMM and Beta-Binomial HGLM with a random effect of years. The random effect in both models shows that there is a variation in the percentage of poor people in years, but the variabilitybetween years cannot be shown by the data. Likewise, the modeling carried out on the percentage of poor population data gives relatively the same results between the model approaches using Binomial GLMM and Beta-Binomial HGLM with clusters random effect. Fixed effects variables and random effects have a significant effect on the percentage of poor people for all models. The model obtained by involving all fixed effects and random effects (clusters) is the best model because itprovides the smallest CAIC value compared to models using random effects (Years). [ABSTRACT FROM AUTHOR]

The Generalized Linear Model method and the Generalized Linear Mixed Model method are methods that delete the assumption of data normality for the response variables. The two methods are compared and applied to the Human Development Index (HDI) panel data to see the best model and determine what factors affect HDI. The predictor variables used include: GRDP, ratio of elementary school teachers to elementary students, ratio of junior high school teachers to junior high school students, ratio of high school teachers to high school students, number of health workers, population and poverty ratio. The data source comes from BPS by taking three provinces classified into provinces with very high HDI represented by DKI Jakarta province, Province with high HDI represented by West Java province, and provinces with moderate HDI represented by NTB province. The results showed that neither the GLM method nor the GLMM method could be used to model HDI data. The factors that influence the HDI for the data for the provinces of Jakarta and NTB are the GRDP factor, the ratio of elementary school teachers to elementary students, the ratio of junior high school teachers to junior high school students, the number of health workers, and the total population. In the data for the province of Jakarta, an anomaly in economic theory is found which states that an increase in GRDP causes a decrease in the HDI value. This indicates that the GRDP factor for the DKI Jakarta province isnot sufficient to represent the economic dimension as one of the indicators in forming the HDI value. The factors that influence the HDI value in the province of West Java are the GRDP factor and the poverty ratio, while other factors have no significant effect on the HDI value. [ABSTRACT FROM AUTHOR]

The objective of this study is to model the Generalized Linear Mixed Model (GLMM) on the Consumer Price Index (CPI) data for 34 provinces as panel data in Indonesia. This modeling aims to predict and to see the causes of the CPI movement. This paper contains the formulation, interpretation and inference of the formed GLMM model. The modeling results show that the CPI response variable is assumed to follow the Gamma distribution with a regional random effect better than other models in this paper, this is indicated by the smaller AIC value of 70.2. This study provides a theoretical contribution to dealing with nonnormal distributed data in the CPI as panel data in Indonesia (in this case, we assume that the CPI have Gamma and Lognormal Distributed). In addition, this research is also practically useful for determining the most suitable modeling for CPI data as panel data in Indonesia. [ABSTRACT FROM AUTHOR]