Penerapan Regresi Logistik Multinomial untuk Analisis Model Tingkat Depresi pada Lansia

Depresi merupakan salah satu gangguan kesehatan mental yang mengganggu kehidupan sehari-hari dan pada tingkat yang paling parah dapat menyebabkan seseorang bunuh diri. Menurut WHO 280 juta orang di dunia mengalami depresi, dimana sebesar   depresi dialami oleh lansia dan di Indonesia  lansia mengalami depresi. Penelitian ini bertujuan untuk menganalisis model untuk mengetahui model terbaik, mengetahui faktor-faktor yang mempengaruhi tingkat depresi pada lansia, dan ketepatan klasifikasi model. Metode yang digunakan adalah metode regresi logistik multinomial, yang merupakan salah satu metode analisis data untuk mencari hubungan antara variabel respon yang memiliki lebih dari dua kategori atau polychotomous terhadap variabel prediktor. Data pada penelitian ini menggunakan data dari 90 responden lansia pada penelitian sebelumnya dengan mengisi kuesioner mengenai uji GDS-15 (Geriatric Depression Scale) di Puskesmas Bandar Khalipah untuk mengetahui tingkat depresi pada lansia, kemudian data disesuaikan dengan metode regresi logistik multinomial. Kemudian model terbaik didapatkan dari nilai Akaike Information Criterion (AIC) terkecil. Hasil penelitian ini menunjukkan bahwa didapatkan enam model regresi logistik multinomial dengan faktor usia adalah faktor yang paling signifikan dalam mempengaruhi tingkat depresi lansia dan nilai Akaike Information Criterion (AIC) yang didapatkan menunjukkan bahwa model 1 dengan nilai Akaike Information Criterion (AIC) sebesar 109.836 merupakan model terbaik dengan nilai akurasi sebesar 61.11%. Kata Kunci: Akaike Information Criterion (AIC), Depresi, Lansia, Regresi Logistik Multinomial.   &nbsp

Model selection in continuous test norming with GAMLSS

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box–Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box–Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test

S-estimation and a robust conditional Akaike information criterion for linear mixed models.

We study estimation and model selection on both the fixed and the random effects in the setting of linear mixed models using outlier robust S-estimators. Robustness aspects on the level of the random effects as well as on the error terms is taken into account. The derived marginal and conditional information criteria are in the style of Akaike's information criterion but avoid the use of a fully specified likelihood by a suitable S-estimation approach that minimizes a scale function. We derive the appropriate penalty terms and provide an implementation using R. The setting of semiparametric additive models fit with penalized regression splines, in a mixed models formulation, fits as a specific application. Simulated data examples illustrate the effectiveness of the proposed criteria.Akaike information criterion; Conditional likelihood; Effective degrees of freedom; Mixed model; Penalized regression spline; S-estimation;

Principal Component Regression Analysis of CO2 Emission

Principal component regression (PCR) model is developed, in this study, for predicting and forecasting the abundance of CO2 emission which is the most important greenhouse gas in the atmosphere that contributes to global warming. The model was compared with supervised principal component regression (SPCR) model and was found to have more predictive power than it using the values of Akaike information criterion (AIC) and Swartz information criterion (SIC) of the models.Keywords: Global warming, CO2, Principal component regression (PCR), Supervised principal component regression (SPCR), Akaike information criterion (AIC) and Swartz information criterion (SIC

Piecewise Regression through the Akaike Information Criterion using Mathematical Programming

In machine learning, regression analysis is a tool for predicting the output variables from a set of known independent variables. Through regression analysis, a function that captures the relationship between the variables is fitted to the data. Many methods from literature tackle this problem with various degrees of difficulty. Some simple methods include linear regression and least squares, while some are more complicated such as support vector regression. Piecewise or segmented regression is a method of analysis that partitions the independent variables into intervals and a function is fitted to each interval. In this work, the Optimal Piecewise Linear Regression Analysis (OPLRA) model is used from literature to tackle the problem of segmented analysis. This model is a mathematical programming approach that is formulated as a mixed integer linear programming problem that optimally partitions the data into multiple regions and calculates the regression coefficients, while minimising the Mean Absolute Error of the fitting. However, the number of regions is a known priori. For this work, an extension of the model is proposed that can optimally decide on the number of regions using information criteria. Specifically, the Akaike Information Criterion is used and the objective is to minimise its value. By using the criterion, the model no longer needs a heuristic approach to decide on the number of regions and it also deals with the problem of overfitting and model complexity

Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion

An improved AIC-based criterion is derived for model selection in general smoothing-based modeling, including semiparametric models and additive models. Examples are provided of applications to goodness-of-fit, smoothing parameter and variable selection in an additive model and semiparametric models, and variable selection in a model with a nonlinear function of linear terms.Statistics Working Papers Serie