scholarly journals The Balakrishnan-Alpha-Beta-Skew-Normal Distribution: Properties and Applications

2021 ◽  
pp. 367-380
Author(s):  
Sricharan Shah ◽  
Partha Jyoti Hazarika ◽  
Subrata Chakraborty ◽  
M. Masoom Ali
Keyword(s):  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Data Fitting ◽  
Skew Normal Distribution ◽  
Distributional Properties ◽  
New Form ◽  
Alpha Beta ◽  
Skew Distribution ◽  
New Distribution

In this paper, a new form of alpha-beta-skew distribution is proposed under Balakrishnan (2002) mechanism and investigated some of its related distributions. The most important feature of this new distribution is that it is versatile enough to support both unimodal and bimodal as well as multimodal behaviors of the distribution. The moments, distributional properties and some extensions of the proposed distribution have also been studied.  Finally, the suitability of the proposed distribution has been tested by conducting data fitting experiment and comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) with the values of some other related distributions. Likelihood Ratio testis used for discriminating between normal and the proposed distributions.

scholarly journals The Log-Balakrishnan-Alpha-Skew-Normal Distribution and its Applications

2020 ◽  
pp. 109-117
Author(s):  
Sricharan Shah ◽  
Subrata Chakraborty ◽  
Partha Jyoti Hazarika ◽  
M Masoom Ali
Keyword(s):  
Likelihood Ratio Test ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Data Fitting ◽  
Ratio Test ◽  
Skew Normal Distribution ◽  
Distributional Properties ◽  
New Form ◽  
Skew Distribution

In this paper, a new form of log-alpha-skew distribution is proposed by the same methodology of Venegas et al. (2016) and investigated some of its related distributions. The moments and distributional properties of the proposed distribution are also discussed. Also, the appropriateness of this distribution are checked by performing the data fitting experiment and comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) with the values of some other known distributions. Likelihood ratio test is used for discriminating between normal and the proposed distributions.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Identificación de un modelo lineal adecuado para evaluar características pre-destete en ganado criollo colombiano Blanco Orejinegro

2021 ◽  
Vol 26 (1) ◽  
pp. 49-56
Author(s):  
Luisa Fernanda Naranjo Guerrero ◽  
Alberiro López Herrera ◽  
Juan Carlos Rincon Florez ◽  
Luis Gabriel González Herrera
Keyword(s):  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Modelo Lineal

La Raza criolla Blanco Orejinegro (BON) tiene un proceso de adaptación de más de 500 años a las condiciones ambientales de Colombia. Se caracteriza por ser una raza doble propósito utilizada para la producción de leche y carne, convirtiéndola en un patrimonio biológico de gran importancia que debe ser estudiado. El objetivo de este estudio fue identificar un modelo lineal adecuado para evaluar características pre-destete en ganado criollo Blanco Orejinegro. Se recolectó y depuró información de pesajes de cuatro hatos de ganado BON. Las características evaluadas fueron peso a los 4 meses (P4M), peso al destete (PD) y ganancia diaria de peso entre los 4 meses y el destete (GDP4M-D). Se evaluaron nueve modelos lineales en los que se incluyeron como efectos fijos los siguientes factores: sexo, hato, mes de pesaje o nacimiento, número de parto, época de pesaje o época de nacimiento (época seca o lluviosa), edad (covariable, efecto fijo y ajustada por regresión), año de pesaje o año de nacimiento y grupo contemporáneo (GC) compuesto por sexo y hato para GDP4M-D y sexo, hato y año de pesaje para P4M y PD, con mínimo cinco observaciones por GC. Para identificar el modelo lineal más adecuado para cada característica se utilizó el valor de AIC (Akaike information criterion), BIC (Bayesian information criterion), coeficiente de determinación (R2) y la suma de cuadrados del error (SCE). El modelo más adecuado para todas las características fue aquel que involucró el GC y edad como efecto fijo para P4M y edad como covariable para PD.

scholarly journals STATISTICAL INTERPRETATION AND OPTIMIZATION OF VALSARTAN FLOATING TABLETS USING BOX-BEHNKEN DESIGN

2019 ◽  
pp. 44-53
Author(s):  
SANKHA BHATTACHARYA
Keyword(s):  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Stability Study ◽  
Therapeutic Window ◽  
Dissolution Time ◽  
Floating Tablets ◽  
Mean Dissolution Time ◽  
Styrene Divinylbenzene

Objective: The main purpose of this study was to formulate and statistically evaluate 300 mg floating tablets of valsartan. Methods: Floating tablets of valsartan was prepared in 16 station rotary punching machine by considering 300 mg of valsartan as drug, 40-60 mg of hydroxypropyl methylcellulose (HPMC) K100M and 20-40 mg of poly (styrene-divinylbenzene) as polymers and 20 mg of sodium bicarbonate as gas generating agents. Since upper stomach has maximum therapeutic window for valsartan absorption, hence Gastroretentive Floating Tablets (GRFTs) was prepared by implementing Box-Bentham Design. The pre and post compression parameters were optimized using Statistica 10 software. From the in vitro buoyancy and drug release studies and interpretation of statistical outcomes viz. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), Dissolution Efficiency (DE), Mean Dissolution Time (MDT), desirability study, it was concluded that batch VF5 formulation was found to be the most optimized formulation. Results: The floating time of VF5 was found to be 132±0.33 sec, in vitro buoyancy time was 18 h, Akaike Information Criterion (AIC) was 54.97, Bayesian Information Criterion (BIC) was 5.13, percentage dissolution efficacy was 56.39%, mean dissolution time was 5.19hr. Further, six-month stability study was performed as per ICH QIA guideline. After performing two-way ANOVA within stability study response variables, it was confirmed that the interaction was most significant. Conclusion: Valsartan floating drug delivery system was successfully developed by considering HPMC K100M and poly (styrene-divinylbenzene) as polymers. Among all the nine batches, VF5 was found to be the best-optimized batch.

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

2021 ◽  
Vol 2021 (1) ◽  
pp. 195-203
Author(s):  
Rahma Rahma Nuryanti ◽  
Tulus Soebagijo
Keyword(s):  
Akaike Information Criterion ◽  
Structural Equation Modelling ◽  
Bayesian Information Criterion ◽  
Structural Equation ◽  
Information Criterion ◽  
Partial Least Square ◽  
Finite Mixture ◽  
Least Square ◽  
Equation Modelling

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.

scholarly journals Modelagem e previsão de temperaturas médias mensais de Rio Paranaíba/MG utilizando modelos de séries temporais

2020 ◽  
Vol 11 (6) ◽  
pp. 251-261
Author(s):  
Frederico Carlos Martins de Menezes Filho
Keyword(s):  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion

Os modelos de séries temporais são largamente utilizados no estudo de variáveis climatológicas como a precipitação, umidade e temperatura. Neste trabalho, aplicou-se a metodologia Box & Jenkins no intuito da obtenção de um modelo estatístico para previsão de valores futuros de temperatura média mensal para a cidade de Rio Paranaíba-MG. Foram utilizados dados de temperatura média de dezesseis anos (janeiro de 2002 a dezembro de 2018) para o ajuste do modelo. Para o período de teste, foram utilizados os dados do ano de 2019. A análise permitiu identificar na série temporal em estudo, a presença dos componentes, tendência e sazonalidade. Diversos modelos do tipo SARIMA (Autorregresivo Integrado e de Médias Móveis Sazonal), ou seja, modelos ARIMA que consideram a sazonalidade observada na série foram ajustados. Dentre os modelos, selecionaram-se os que obtiveram os menores valores dos critérios AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion) e EQM (Erro Quadrático Médio). O modelo escolhido foi o modelo SARIMA (0,1,1) (3,1,0)12 que traduziu bem a dinâmica temporal da série para fins de previsão. O referido modelo obteve um bom ajuste à série de temperaturas médias observadas, apresentando para um horizonte previsto de dozes meses, um valor de 0,50 para o EQM.  

Application of statistical model selection criteria to the Stock Synthesis assessment program

2000 ◽  
Vol 57 (9) ◽  
pp. 1784-1793 ◽  
Author(s):  
S Langitoto Helu ◽  
David B Sampson ◽  
Yanshui Yin
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Model Selection Criteria ◽  
Assessment Program ◽  
Correct Model ◽  
Complex Models ◽  
Multiple Data Sets

Statistical modeling involves building sufficiently complex models to represent the system being investigated. Overly complex models lead to imprecise parameter estimates, increase the subjective role of the modeler, and can distort the perceived characteristics of the system under investigation. One approach for controlling the tendency to increased complexity and subjectivity is to use model selection criteria that account for these factors. The effectiveness of two selection criteria was tested in an application with the stock assessment program known as Stock Synthesis. This program, which is often used on the U.S. west coast to assess the status of exploited marine fish stocks, can handle multiple data sets and mimic highly complex population dynamics. The Akaike information criterion and Schwarz's Bayesian information criterion are criteria that satisfy the fundamental principles of model selection: goodness-of-fit, parsimony, and objectivity. Their ability to select the correct model form and produce accurate estimates was evaluated in Monte Carlo experiments with the Stock Synthesis program. In general, the Akaike information criterion and the Bayesian information criterion had similar performance in selecting the correct model, and they produced comparable levels of accuracy in their estimates of ending stock biomass.

scholarly journals Performance comparison of model selection criteria by generated experimental data

2018 ◽  
Vol 16 ◽  
pp. 02006
Author(s):  
Radoslav Mavrevski ◽  
Peter Milanov ◽  
Metodi Traykov ◽  
Nevena Pencheva
Keyword(s):  
Experimental Data ◽  
Model Selection ◽  
Mathematical Models ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Data Fitting ◽  
Performance Comparison ◽  
Artificial Data ◽  
Criteria For Evaluation

In Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical models for data fitting. The main objectives of this study are: (1) to fitting artificial experimental data with different models with increasing complexity; (2) to test whether two known criteria as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can correctly identify the model, used to generate the artificial data and (3) to assess and compare empirically the performance of AIC and BIC.

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