Survival Analysis of Food Security in Asian Countries

This study focuses on using the survival analysis on food security application. The technique examines the effects of covariates on food insecurity among Asian countries in the period of 40 years since 1961. The analysis is carried out in order to determine the 'warning sign' of food insecurity condition. The data sources are from FA0 and World Bank online database which include some particulars of 32 Asian countries. It is observed that 21 of 32 (65.62%) countries experienced insecurity food condition. The remaining are censored observations (34.38%). The stepwise Cox's regression is used to select among the 24 independent covariates that are deemed to be significant contribution to the model. Initial run of the SAS code finds that six covariates are significant. Based on the adopted model, at each time point, the West Asian region are found to be more likely to have insecurity food condition compared to those countries in the other regions. Furthermore, the occurrence of food security for East Asia countries are more likely than for those in the other region. Meanwhile, it can also be seen that countries in Lower-middle income group are more likely to reach insecurity food condition than those in the other group. The analysis also shows that the high income countries have high risk of exposure to insecurity food condition. Since Cox regression analysis has the basic assumption of proportionality, the model was tested whether it meets this condition. We use graphical method and formal test of this assumption . In the presence of ties, the ties-handling method of Breslow, Efron, Exact, and Discrete are compared with respect to Wald statistics, parameter estimate, the hazard ratio, and p-value. The availability of the determined dataset as in allows assessing categories of food insecurity; Low, Medium, or High, which is useful to describe the nature of the food insecurity conditions. Based on the analysis, we are able to find variables that play important role on each stage of food insecurity condition of each country

Robust Estimation Methods And Outlier Detection In Mediation Models

Mediation models refer to the relationships among three variables: an independent variables (IV), a potential mediating variable (M), and a dependent variable (DV). When the relationship between the dependent variable (DV) and an independent variables (IV) can be accounted for by an intermediate variable M, mediation is said to occur. Simple mediation model consists of three regression equations. The Ordinary Least Squares (OLS) method is often use to estimate the parameters of the mediation model. However, due to the fact that outliers have an unduly effect on the OLS estimates, we propose to incorporate robust M and MM estimator which are not easily affected by outliers, in the estimation of the mediation model which is called RobSim1 and RobSim2, respectively. The numerical example indicates that various types of contamination in the simulated data have arbitrarily large effect on the OLS estimates and the Sobel test. The MM-estimator incorporated in RobSim2 has improved the precision of the indirect effect of mediation model. The overall analysis clearly shows that the Simple Mediation Model based on RobSim2 is prominently the most excellent result, because it is able to withstand various contamination in the m , x , and y - axes (direction). There is also concern not only when the data contain observations that are extreme in the response variable but also in the regressor space, namely the leverage points. A new measure for the identification of high-leverage point is called Diagnostic Robust Generalized Potentials (DRGP) which is proposed previously. The DRGP procedures incorporated the Robust Mahanalobis Distance (RMD) based on the minimum volume ellipsoid (MVE) for identifying the set of cases ‘remaining’ (R) and a set of cases ‘deleted’(D), and then diagnostic approach is used to confirm the suspected values. The DRGP procedure uses MAD as its cutoff points. We suggest an alternative method for identification of high leverage points in the mediation model. A modification is made to the DRGP procedure. It was verified that both MAD and n Q have the same breakdown point that is 50%. Nonetheless, the efficiency of the n Q is higher (86%) than the MAD (37%). This work inspired us to incorporate the n Q instead of the MAD in the proposed algorithm. We refer the above new method of identifying potential outliers in mediation analysis as ModDRGP1 where the MAD is incorporated in the second step of the ModDRGP1 algorithm. In this thesis we also propose another DRGP, which has modified step 2 and step 4 for identifying potential outliers in mediation model. We called the second proposed method as ModDRGP2

On the performance of stepwise selection method in the presence of outliner

Stepwise regression is one of common procedures of variable selection in linear regression model when we have many independent variables. The procedure is known to have good performance under least squares methods when there is no outlier in the data. In this article, we conduct a study based on an empirical data to observe the performance of the stepwise regression in the presence of a single outlier in the data. We found that the presence of a single outlier may bother the selecting variables in step(s) of the stepwise regression. This leads to have misinterpretation of decision makers

Skenario Kebijakan Tentang Ruang Terbuka Hijau Di Kota Batu: Suatu Pendekatan Simulasi

Steps to create environmental comfort in Batu City, East Java, need to support sustainable development. This research aims to develop policy scenarios related to simulation-based Green Open Space (RTH) planning to realize Batu City's environmental sustainability. The study of stakeholder preferences for the function of green space is carried out by digging primary data from the relevant parties (stakeholders) using interviews and observations. The green open space in Batu City is decreasing due to the conversion of the RTHK function into a built area. The change in RTHK was caused by the implementation of Batu City development activities which were more inclined to infrastructure development as well as physical facilities and infrastructure. Policy analysis is carried out by conducting simulations (changes to model parameters) and then observing their behavior. Several green open space planning scenarios were carried out using the Powersim constructor software. Several scenarios are related to green open space planning in Batu City, including free scenario, moderate scenario, and sustainable scenario. Of the three scenarios, the sustainable scenario is more suitable because the increase in land ares used in the sustainable scenario is relatively controlled. There are efforts to allocate green open space on residential land, industrial land, social and social facilities land, trade and service land every year to reduce the decrease in green open space

A comparison between classical and robust method in a factorial design in the presence of outlier

Analysis of Variance (ANOVA) techniques which is based on classical Least Squares (LS) method requires several assumptions, such as normality, constant variances and independency. Those assumptions can be violated due to several causes, such as the presence of an outlying observation. There are many evident in literatures that the LS estimate is easily affected by outliers. To remedy this problem, a robust procedure that provides estimation, inference and testing that are not influenced by outlying observations is put forward. A well-known approach to handle dataset with outliers is the M-estimation. In this study, both classical and robust procedures are employed to data of a factorial experiment. The results signify that the classical method of least squares estimates instead of robust methods lead to misleading conclusion of the analysis in factorial designs

Diagnostic-robust generalized potentials for identifying high leverage points in mediation analysis

Due to the fact that mediation model involves several linear regression equations, there is concern not only when the data contain observations that are extreme in the response variable but also in the regressor space, namely the leverage points. The Diagnostic Robust Generalized Potentials (DRGP) procedure in multiple linear regression incorporated the Robust Mahanalobis Distance based on the minimum volume ellipsoid and uses Median Absolute Deviation as its cut-off points. In this paper, a slight modification to the DRGP is proposed and we call it ModDRGP. The ModDRGP is applied to the mediation model. The performance of our proposed ModDRGP is evaluated based on Monte Carlo simulation study. The simulation results suggest that ModDRGP has improved the accuracy of the identification of high leverage points when the percentage of high leverage points is medium or high. The method can also be used for the identification of high leverage points in multiple mediation models, as well

Standardized simple mediation model : a numerical example

Mediation models figure out how an effect occurred by hypothesizing a causal sequence. For a simple mediation model, a causal sequence is described in which an independent variable causes the mediator which sequentially causes the dependent variable. In this article, we tried to introduce to use standardized regression coefficient to the involving the simple mediation model since a standardized coefficient will be more meaningful than an unstandardized coefficients. In this article, we show that in simple mediation model, even though standardized regression coefficients are different from the unstandardized coefficients, but the standardized coefficients maintain the order of magnitude of the unstandardized regression coefficients for the simple mediation model

Estimating bias and RMSE of indirect effects using rescaled residual bootstrap in mediation analysis.

It is a common practice to estimate the parameters of mediation model by using the Ordinary Least Squares (OLS) method. The construction of T statistics and confidence interval estimates for making inferences on the parameters of a mediation model, particularly the indirect effect, is usually are based on the assumption that the estimates are normally distributed. Nonetheless, in practice many estimates are not normal and have a heavy tailed istribution which may be the results of having outliers in the data. An alternative approach is to use bootstrap method which does not rely on the normality assumption. In this paper, we proposed a new bootstrap procedure of indirect effect in mediation model which is resistant to outliers. The proposed approach was based on residual bootstrap which incorporated rescaled studentized residuals, namely the Rescaled Studentized Residual Bootstrap using Least Squares (ReSRB). The Monte Carlo simulations showed that the ReSRB is more efficient than some existing methods in the presence of outliers

Procedures of generating a true clean data in simple mediation analysis

Simulation study is very important in model validation. It is invaluable and versatile tool especially in statistical problems and modeling where analytical technique is inadequate. In fitting to a model, problems will raise when there exists one or more high-leverage points in the data set. Due to the fact that the presence high-leverage points are commonly occurred in models fitting, we propose a new algorithm in mediation analysis which guarantees clean data set without any high-leverage points. The new proposed algorithm employs the newly proposed Modified Diagnostic-Robust Generalized Potentials. By incorporating ModDRGP in the proposed algorithm has rectified the problem of having high leverage points in the generated clean data set, especially for mediation models. We found that in 10000 simulation runs, only about 31.14% of the cleangenerated dataset were obtained by direct simulation. The results also reveal that as the sample size increases, the percentage of obtaining direct clean dataset decreases