A Study of Count Regression Models for Mortality Rate

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

SEM-PLS Untuk Analisis Struktur Kemiskinan Pada Masa Pandemi Covid-19

Pandemi Covid-19 menimbulkan berbagai dampak khususnya pada aspek perekonomian. Kondisi perekonomian yang sulit ini menyebabkan pendapatan masyarakat mengalami penurunan, dan menyebabkan jumlah penduduk miskin meningkat. Jumlah penduduk miskin bertambah sebanyak 1,28 juta orang pada tahun 2020. Provinsi Jawa Timur merupakan provinsi yang memiliki tingkat kemiskinan (10,20 persen) sedikit lebih tinggi daripada nasional (10,19 persen) pada tahun 2020. Hal ini dikarenakan adanya dampak pandemi yang menyebabkan hilangnya lapangan pekerjaan dan meningkatnya angka kemiskinan. Penelitian ini akan menganalisis struktur kemiskinan di Provinsi Jawa Timur pada tahun 2020. Tujuan penelitian ini adalah untuk melihat struktur kemiskinan di Provinsi Jawa Timur pada tahun 2020. Metode analisis yang digunakan dalam penelitian ini adalah Structural Equation Modelling (SEM) berbasis komponen yaitu Partial Least Square (PLS). Pada model persamaan struktural terdapat 4 jalur yang signifikan, yaitu pengaruh variabel kesehatan terhadap variabel pendidikan, pengaruh variabel kesehatan dan variabel pendidikan terhadap ekonomi, serta pengaruh variabel ekonomi terhadap variabel kemiskinan. Hasil Analisis Pengelompokan dengan Finite Mixture Partial Least Square berdasarkan kriteria Akaike Information Criterion (AICk), Consistent Akaike Information Criterion (CAICk) dan Bayesian Information Criterion ( BICk) serta Normal Entrophy (EN) diperoleh hasil terbaik yang terbentuk adalah 2 segmen. Sehingga dari 38 kabupaten/kota di wilayah Provinsi Jawa Timur dapat dikelompokkan menjadi 2 segmen. Segmen Pertama sebesar 91,9 persen dari jumlah kabupaten/kota, dan Segmen Kedua sebesar 8,1 persen dari jumlah kabupaten/kota di wilayah Jawa Timur. Kabupaten/kota yang berada pada segmen kedua adalah Kabupaten Situbondo, Kabupaten Nganjuk dan Kota Kediri. Sementara 35 kabupaten/kota lainnya berada di segmen pertama.

A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Latent transition analysis is an informative statistical tool for depicting heterogeneity in learning as latent profiles. We present a Monte Carlo simulation study to guide researchers in selecting fit indices for identifying the correct number of profiles. We simulated data representing profiles of learners within a typical pre- post- follow up-design with continuous indicators, varying sample size (N from 50 to 1000), attrition rate (none/10% per wave), and profile separation (entropy; from .73 to .87). Results indicate that the most commonly used fit index, the Bayesian information criterion (BIC), and the consistent Akaike information criterion (CAIC) consistently underestimate the real number of profiles. A combination of the AIC or the AIC3 with the adjusted Bayesian Information Criterion (aBIC) provides the most precise choice for selecting the number of profiles and is accurate with sample sizes of at least N = 200. The AIC3 excels starting from N = 500. Results were mostly robust towards differing numbers of time points, profiles, indicator variables, and alternative profiles. We provide an online tool for computing these fit indices and discuss implications for research.

Joint inversion of strain and tilt data using the Akaike’s Bayesian information criterion to map detailed slip distributions of short-term slow slip events

Along the Nankai Trough subduction zone, southwest Japan, short-term slow slip events (SSEs) are commonly detected in strain and tilt records. These observational data have been used in rectangular fault models with uniform slip to analyze SSEs; however, the assumption of uniform slip precludes the possibility of mapping the slip distribution in detail. We report here an inversion method, based on the joint use of strain and tilt data and evaluated in terms of the Akaike’s Bayesian information criterion (ABIC), to estimate the slip distributions of short-term SSEs on the plate interface. Tests of this method yield slip distributions with smaller errors than are possible with the use of strain or tilt data alone. This method provides detailed spatial slip distributions of short-term SSEs including probability estimates, enabling improved monitoring of their locations and amounts of slip.

Modelling Claim Frequency in Insurance Using Count Models

Background: In modelling claim frequency in actuary science, a major challenge is the number of zero claims associated with datasets. Aim: This study compares six count regression models on motorcycle insurance data. Methodology: The Akaike Information Criteria (AIC) and the Bayesian Information Criterion (BIC) were used for selecting best models. Results: Result of analysis showed that the Zero-Inflated Poisson (ZIP) with no regressors for the zero component gives the best predictive ability for the data with the least BIC while the classical Negative Binomial model gives the best result for explanatory purpose with the least AIC.

Using Model Selection Criteria to Choose the Number of Principal Components

The use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

Implementasi Metode TwoStep Clustering untuk Klasterisasi Karakteristik Akademik Mahasiswa

Salah satu algoritma clustering yang paling banyak dipakai adalah K-Means dimana algoritma ini membutuhkan masukan jumlah klaster yang ingin dibentuk. Pada kenyataannya jumlah klaster yang tepat tidak bisa diketahui sehingga pemilihan nilai k bergantung pada subyektifitas peneliti. Kemudian algoritma K-Means hanya bisa menangani atribut dalam bentuk numerik kontinyu padahal ada atribut dalam bentuk kategorikal atau campuran keduanya. Pada penelitian ini dilakukan pengelompokkan data akademik mahasiswa dengan menggunakan algoritma twostep clustering yang dapat menentukan jumlah klaster secara otomatis dan dapat menangani atribut dalam bentuk kategorikal, numerik kontinyu atau campuran keduanya. Metode twostep clustering diterapkan pada data mahasiswa angkatan 2008-2019 dengan analisis diterapkan pada setiap angkatannya. Penelitian ini menghasilkan klaster-klaster yang mencerminkan tingkat heterogenitas setiap angkatan mahasiswa. Klaster-klaster yang didapat merupakan klaster yang optimal setelah diukur menggunakan Bayesian Information Criterion dan Ratio Distance Measure.

Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood

In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, SG is considered as an implicit probability distribution estimation under the assumption that there exists a true contextual distribution among words. Therefore, we apply information criteria with the aim of selecting the best dimensionality so that the corresponding model can be as close as possible to the true distribution. We examine the following information criteria for the dimensionality selection problem: the Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sequential Normalized Maximum Likelihood (SNML) criterion. SNML is the total codelength required for the sequential encoding of a data sequence on the basis of the minimum description length. The proposed approach is applied to both the original SG model and the SG Negative Sampling model to clarify the idea of using information criteria. Additionally, as the original SNML suffers from computational disadvantages, we introduce novel heuristics for its efficient computation. Moreover, we empirically demonstrate that SNML outperforms both BIC and AIC. In comparison with other evaluation methods for word embedding, the dimensionality selected by SNML is significantly closer to the optimal dimensionality obtained by word analogy or word similarity tasks.