Pakistan Journal of Statistics and Operation Research最新文献_第6页

Prediction of KLCI Index Through Economic LASSO Regression Model and Model Averaging 利用经济LASSO回归模型和模型平均预测KLCI指数

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4214

Khuneswari Gopal Pillay, Soh Pei Lin

{"title":"Prediction of KLCI Index Through Economic LASSO Regression Model and Model Averaging","authors":"Khuneswari Gopal Pillay, Soh Pei Lin","doi":"10.18187/pjsor.v19i1.4214","DOIUrl":"https://doi.org/10.18187/pjsor.v19i1.4214","url":null,"abstract":"The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and the complexity in terms of identifying the factors that have a substantial impact on the Malaysian stock market has always been a contentious issue. In this study, the macroeconomic factors of exchange rate, interest rate, gold price, consumer price index, money supply M1, M2, and M3, industrial production, and oil price were discussed by using economic LASSO regression and Bayesian Model Averaging (BMA) with monthly average and monthly end time-series data spanning from January 2015 to June 2021, with a total of 78 observations by using the R Studio. The findings demonstrate that month-end data is better suited for stock market prediction than month-average data and that the BMA model is more suitable than the LASSO model, as seen by lower Mean Square Error of Prediction, MSE(P) and Residual Mean Square Error of Prediction, RMSE(P) values. The exchange rate, gold price, and money supply have a negative association with the dependent variables, while the consumer price index has a positive relationship associated with the dependent variables. The consumer price index is the most significant contributing factor, whereas gold price is the least significant. The result depicted that the KLCI index has no significant relationship with the variables interest rate, money supply M2, M1, industrial production index, and oil price. In conclusion, investors could specifically focus on the positive contributor and put lesser attention on improving their portfolio return.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42257106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiclass Forecasting on Panel Data Using Autoregressive Multinomial Logit and C5.0 Decision Tree 基于自回归多项式Logit和C5.0决策树的面板数据多类预测

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4053

Muhlis Ardiansyah, Hari Wijayanto, Anang Kurnia, A. Djuraidah

{"title":"Multiclass Forecasting on Panel Data Using Autoregressive Multinomial Logit and C5.0 Decision Tree","authors":"Muhlis Ardiansyah, Hari Wijayanto, Anang Kurnia, A. Djuraidah","doi":"10.18187/pjsor.v19i1.4053","DOIUrl":"https://doi.org/10.18187/pjsor.v19i1.4053","url":null,"abstract":"Panel data is commonly used for the numerical response variables, while the literature for forecasting categorical variables on the panel data structure is still challenging to find. Forecasting is important because it is helpful for government policies. This study aimed to forecast multiclass or categorical variables on the panel data structure. The proposed forecasting models were autoregressive multinomial logit and autoregressive C5.0. The strategy applied so that the two models could be used for forecasting was to add autoregressive effects and fixed predictor variables such as location, time, strata, and month of observations. The autoregressive effect was assumed to be a fixed effect and treated as a dummy variable. The data used was the category of land conditions through The Area Sampling Frame (ASF) survey conducted by the BPS-Statistics Indonesia. The evaluation of both models was based on classification and forecasting performance. Classification performance was obtained by dividing the dataset into 75% training data for modeling and 25% test data for validation and then repeated 200 times. The classification results showed that the autoregressive C5.0 accuracy was 86.48%, while the autoregressive multinomial logit was 83.97%. A comparison of forecasting performance was obtained by dividing the data into training and testing based on the time sequence. The result showed that the forecasting performance was worse than the classification performance. Autoregressive C5.0 had an accuracy of 77.43%, while autoregressive multinomial logit had 77.77%.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47354077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Asymptotic Normality of the Conditional Hazard Rate Function Estimator for Right Censored Data under Association 关联条件下右截断数据条件危险率函数估计的渐近正态性

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.3740

Samra Dhiabi, Ourida Sadki

引用次数: 0

Short-Term Insurance Claims Payments Forecasting with Holt-Winter Filtering and Residual Analysis 基于Holt-Winter滤波和残差分析的短期保险赔付预测

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4215

Moustafa Salem, Mohamed G. Khalil

引用次数: 4

A Proposed Method for Finding Initial Solutions to Transportation Problems 一种寻找交通问题初始解的方法

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4196

Joseph Ackora-Prah, Valentine Acheson, Emmanuel Owusu-Ansah, Seth K. Nkrumah

引用次数: 0

The Multimodal Extension of the Balakrishnan Alpha Skew Normal Distribution: Properties and Applications Balakrishnan-Alpha斜正态分布的多模态推广：性质和应用

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.4019

Sricharan Shah, Partha Jyoti Hazarika, Dimpal Pathak, Subrata Chakraborty, M. Masoom Ali

引用次数: 1

Mean Estimation of a Sensitive Variable under Measurement Errors using Three-Stage RRT Model in Stratified Two-Phase Sampling 分层两相采样中三阶段RRT模型对测量误差下敏感变量的均值估计

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-06 DOI: 10.18187/pjsor.v19i1.3929

Ronald O Onyango, Brian Oduor, Francis Odundo

引用次数: 0

Bayesian estimation for the type-I hybrid xgamma distribution using asymmetric loss function 使用非对称损失函数的i型混合xgamma分布的贝叶斯估计

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-04 DOI: 10.18187/pjsor.v19i1.2808

A. Yadav

{"title":"Bayesian estimation for the type-I hybrid xgamma distribution using asymmetric loss function","authors":"A. Yadav","doi":"10.18187/pjsor.v19i1.2808","DOIUrl":"https://doi.org/10.18187/pjsor.v19i1.2808","url":null,"abstract":"This article proposes the Bayes estimation of the parameter and reliability function for xgamma distribution in the presence of type-I hybrid censored observations. The Bayes estimate of the parameter has been obtained by assuming informative and non-informative priors using general entropy loss function. Obviously, censoring adds difficulties in estimation procedure; hence the Bayes estimators computed with type-I hybrid censored observation under the mentioned prior often do not assume any standard form. Therefore, Bayes estimates are computed using Tierney-Kadane approximation and Markov Chain Monte Carlo numerical technique. Further, different interval estimates namely asymptotic confidence interval, bootstrap confidence interval and highest posterior density interval along with the width of the interval and coverage probability are also discussed. The maximum likelihood estimate for the same has also been computed using non- linear maximization iterative procedure and compared with corresponding Bayes estimates using Monte Carlo simulations. The comparison of the estimators are made in terms of average loss over whole sample space and corresponding length of the interval. lastly, one medical data set has been considered for the real application of the proposed study.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47450467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Inference for a Weighted Bilal Distribution: Regression Model 加权胆汁分布的贝叶斯推断：回归模型

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-04 DOI: 10.18187/pjsor.v19i1.4140

Yupapin Atikankul

引用次数: 0

Approximate MLEs for the location and scale parameters of the Poisson-half-logistic distribution 泊松半逻辑分布的位置和尺度参数的近似MLE

IF 1.5

Pakistan Journal of Statistics and Operation Research Pub Date : 2023-03-04 DOI: 10.18187/pjsor.v19i1.4018

M. Niaparast, Leila Esmaeili

{"title":"Approximate MLEs for the location and scale parameters of the Poisson-half-logistic distribution","authors":"M. Niaparast, Leila Esmaeili","doi":"10.18187/pjsor.v19i1.4018","DOIUrl":"https://doi.org/10.18187/pjsor.v19i1.4018","url":null,"abstract":"Recently, the application of compound distributions has increased due to the flexibility in fitting to actual data in various fields such as economics, insurance, etc. Poisson-half-logistic distribution is one of these distributions with an increasing-constant hazard rate that can be used in parallel systems and complementary risk models. Because of the complexity of the form of this distribution, it is not possible to obtain classical parameter estimates (such as MLE) by the analytical method for the location and scale parameters. We present a simple way of deriving explicit estimators by approximating the likelihood equations appropriately. This paper presents AMLE (Approximate MLE) method to obtain the location and scale parameters estimation. Using simulation, we show that this method is as efficient as the maximum likelihood estimators (MLEs), we obtain the variance of estimators from the inverse of the observed Fisher information matrix, and we see that when sample size increases bias and variance of these estimators, MSEs of parameters decrease. Finally, we present a numerical example to illustrate the methods of inference developed here.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42655374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0