Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.3955
Zakir Hussain Wani Jana, S. Rizvi, Manish Sharma, M. Bhat, Saqib Mushtaq
In this article, a class of Poisson-regression based estimators has been proposed for estimating the finite population mean in simple random sampling without replacement (SRSWOR). The Poisson-regression model is the most common method used to model count responses in many studies. The expression for bias and mean square error (MSE) of proposed class of estimators are obtained up to first order of approximation. The proposed estimators have been compared theoretically with the existing estimators, and the condition under which the proposed class of estimators perform better than existing estimators have been obtained. Two real data sets are considered to assess the performance of the proposed estimators. Numerical findings confirms that the proposed estimators dominate over the existing estimators such as Koc (2021) and Usman et al. (2021) in terms of mean squared error.
{"title":"Modified Regression Estimators for Improving Mean Estimation -Poisson Regression Approach","authors":"Zakir Hussain Wani Jana, S. Rizvi, Manish Sharma, M. Bhat, Saqib Mushtaq","doi":"10.18187/pjsor.v18i4.3955","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.3955","url":null,"abstract":"In this article, a class of Poisson-regression based estimators has been proposed for estimating the finite population mean in simple random sampling without replacement (SRSWOR). The Poisson-regression model is the most common method used to model count responses in many studies. The expression for bias and mean square error (MSE) of proposed class of estimators are obtained up to first order of approximation. The proposed estimators have been compared theoretically with the existing estimators, and the condition under which the proposed class of estimators perform better than existing estimators have been obtained. Two real data sets are considered to assess the performance of the proposed estimators. Numerical findings confirms that the proposed estimators dominate over the existing estimators such as Koc (2021) and Usman et al. (2021) in terms of mean squared error.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43327939","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}
Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.3369
T. Hasan, Syed Adil Hussain
In Industrial and Pharmaceutical experiments it is desired to have best predictions of the response on the basis of small amount of data. Mixture experiment generally aims to predict the response(s) for all possible mixture blends. When we compute optimal design for mixture response surface we must focus on prediction capability of the design. The conventional optimal criteria, such as D-, A- and E-optimality are not suitable for determining the prediction capability of designs. As I-optimal design minimizes the average variance of prediction over the mixture region, so it clearly focuses on prediction capability of the design. Hence I-optimal criterion seems to be more appropriate in this conjecture. In this paper we propose the construction of I-optimal mixture designs for a quadratic Scheffé’s and Darroch and Waller’s model in three and four components, using two orthogonal blocks. I-efficiency of designs is compared with the I-efficiency of D-optimal designs for Scheffé’s and Darroch and Waller’s models.
{"title":"I-optimal Designs for three and four component mixture models in orthogonal blocks","authors":"T. Hasan, Syed Adil Hussain","doi":"10.18187/pjsor.v18i4.3369","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.3369","url":null,"abstract":"In Industrial and Pharmaceutical experiments it is desired to have best predictions of the response on the basis of small amount of data. Mixture experiment generally aims to predict the response(s) for all possible mixture blends. When we compute optimal design for mixture response surface we must focus on prediction capability of the design. The conventional optimal criteria, such as D-, A- and E-optimality are not suitable for determining the prediction capability of designs. As I-optimal design minimizes the average variance of prediction over the mixture region, so it clearly focuses on prediction capability of the design. Hence I-optimal criterion seems to be more appropriate in this conjecture. In this paper we propose the construction of I-optimal mixture designs for a quadratic Scheffé’s and Darroch and Waller’s model in three and four components, using two orthogonal blocks. I-efficiency of designs is compared with the I-efficiency of D-optimal designs for Scheffé’s and Darroch and Waller’s models.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45842844","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}
Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.3748
Z. Algamal, N. Hammood
The ridge regression model has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The logistic regression model is a well-known model in application when the response variable is binary data. However, it is known that multicollinearity negatively affects the variance of maximum likelihood estimator of the logistic regression coefficients. To address this problem, a logistic ridge estimator has been proposed by numerous researchers. In this paper, a new Jackknifing logistic ridge estimator (NLRE) is proposed and derived. The idea behind the NLRE is to get diagonal matrix with small values of diagonal elements that leading to decrease the shrinkage parameter and, therefore, the resultant estimator can be better with small amount of bias. Our Monte Carlo simulation results suggest that the NLRE estimator can bring significant improvement relative to other existing estimators. In addition, the real application results demonstrate that the NLRE estimator outperforms both logistic ridge estimator and maximum likelihood estimator in terms of predictive performance.
{"title":"A new Jackknifing ridge estimator for logistic regression model","authors":"Z. Algamal, N. Hammood","doi":"10.18187/pjsor.v18i4.3748","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.3748","url":null,"abstract":"The ridge regression model has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The logistic regression model is a well-known model in application when the response variable is binary data. However, it is known that multicollinearity negatively affects the variance of maximum likelihood estimator of the logistic regression coefficients. To address this problem, a logistic ridge estimator has been proposed by numerous researchers. In this paper, a new Jackknifing logistic ridge estimator (NLRE) is proposed and derived. The idea behind the NLRE is to get diagonal matrix with small values of diagonal elements that leading to decrease the shrinkage parameter and, therefore, the resultant estimator can be better with small amount of bias. Our Monte Carlo simulation results suggest that the NLRE estimator can bring significant improvement relative to other existing estimators. In addition, the real application results demonstrate that the NLRE estimator outperforms both logistic ridge estimator and maximum likelihood estimator in terms of predictive performance.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48881548","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}
Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.4212
N. Alam, N. Ramli, Adie Safian Ton Mohamed, Noor Izyan Mohamad Adnan
Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-i-fuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed.
{"title":"Integration of 4253HT Smoother with Intuitionistic Fuzzy Time Series Forecasting Model","authors":"N. Alam, N. Ramli, Adie Safian Ton Mohamed, Noor Izyan Mohamad Adnan","doi":"10.18187/pjsor.v18i4.4212","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.4212","url":null,"abstract":"Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-i-fuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41572424","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}
Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.4190
Basma J. Ahmed, C. Chesneau, M. M. Ali, H. Yousof
When a life test is terminated at a predetermined time to decide whether to accept or refuse the submitted batches, the types of group sampling inspection plans (single, two, and multiple-stages) are introduced. The tables in this study give the optimal number of groups for various confidence levels, examination limits, and values of the ratio of the determined experiment time to the fixed percentile life. At various quality levels, the operating characteristic functions and accompanying producer's risk are derived for various types of group sampling inspection plans. At the determined producer's risk, the optimal ratios of real percentile life to a fixed percentile life are obtained. Three case studies are provided to illustrate the processes described here. Comparisons of single-stage and iterative group sampling plans are introduced. The first, second, and third sample minimums must be used to guarantee that the product's stipulated mean and median lifetimes are reached at a certain degree of customer trust. The suggested sample plans' operational characteristic values and the producer's risk are given. In order to show how the suggested approaches based on the mean life span and median life span of the product may function in reality, certain real-world examples are examined.
{"title":"Amputated Life Testing for Weibull-Fréchet Percentiles: Single, Double and Multiple Group Sampling Inspection Plans with Applications","authors":"Basma J. Ahmed, C. Chesneau, M. M. Ali, H. Yousof","doi":"10.18187/pjsor.v18i4.4190","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.4190","url":null,"abstract":"When a life test is terminated at a predetermined time to decide whether to accept or refuse the submitted batches, the types of group sampling inspection plans (single, two, and multiple-stages) are introduced. The tables in this study give the optimal number of groups for various confidence levels, examination limits, and values of the ratio of the determined experiment time to the fixed percentile life. At various quality levels, the operating characteristic functions and accompanying producer's risk are derived for various types of group sampling inspection plans. At the determined producer's risk, the optimal ratios of real percentile life to a fixed percentile life are obtained. Three case studies are provided to illustrate the processes described here. Comparisons of single-stage and iterative group sampling plans are introduced. The first, second, and third sample minimums must be used to guarantee that the product's stipulated mean and median lifetimes are reached at a certain degree of customer trust. The suggested sample plans' operational characteristic values and the producer's risk are given. In order to show how the suggested approaches based on the mean life span and median life span of the product may function in reality, certain real-world examples are examined.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43914024","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}
Financial crisis prediction is a critical issue in the economic phenomenon. Correct predictions can provide the knowledge for stakeholders to make policies to preserve and increase economic stability. Several approaches for predicting the financial crisis have been developed. However, the classification model's performance and prediction accuracy, as well as legal data, are insufficient for usage in real applications. So that, an efficient prediction model is required for higher performance results. This paper adopts a novel two-hybrid intelligent prediction model using an Artificial Neural Network (ANN) for prediction and Particle Swarm Optimization (PSO) for optimization. At first, a PSO technique produces the hyperparameter value for ANN to fit the best architecture. They are weights and thresholds. Then, they are used to predict the performance of the given dataset. In the end, ANN-PSO generates predictions value of crisis conditions. The proposed ANN-PSO model is implemented on time series data of economic conditions in Indonesia. Dataset was obtained from International Monetary Fund and the Indonesian Economic and Financial Statistics. Independent variable data using 13 potential indicators, namely imports, exports, trade exchange rates, foreign exchange reserves, the composite stock price index, real exchange rates, real deposit rates, bank deposits, loan and deposit interest rates, the difference between the real BI rate and the real FED rate, the M1, M2 multiplier, and the ratio of M2 to foreign exchange reserves. Meanwhile, the dependent variable uses the perfect signal value based on the Financial Pressure Index. A detailed statistical analysis of the dataset is also given by threshold value to convey crisis conditions. Experimental analysis shows that the proposed model is reliable based on the different evaluation criteria. The case studies show that the result for predictive data is basically consistent with the actual situation, which has greatly helped the prediction of a financial crisis.
{"title":"An Intelligent Hybrid Model Using Artificial Neural Networks and Particle Swarm Optimization Technique For Financial Crisis Prediction","authors":"Maryam Maryam, Dimas Aryo Anggoro, Muhibah Fata Tika, Fitri Cahya Kusumawati","doi":"10.18187/pjsor.v18i4.3927","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.3927","url":null,"abstract":"Financial crisis prediction is a critical issue in the economic phenomenon. Correct predictions can provide the knowledge for stakeholders to make policies to preserve and increase economic stability. Several approaches for predicting the financial crisis have been developed. However, the classification model's performance and prediction accuracy, as well as legal data, are insufficient for usage in real applications. So that, an efficient prediction model is required for higher performance results. This paper adopts a novel two-hybrid intelligent prediction model using an Artificial Neural Network (ANN) for prediction and Particle Swarm Optimization (PSO) for optimization. At first, a PSO technique produces the hyperparameter value for ANN to fit the best architecture. They are weights and thresholds. Then, they are used to predict the performance of the given dataset. In the end, ANN-PSO generates predictions value of crisis conditions. The proposed ANN-PSO model is implemented on time series data of economic conditions in Indonesia. Dataset was obtained from International Monetary Fund and the Indonesian Economic and Financial Statistics. Independent variable data using 13 potential indicators, namely imports, exports, trade exchange rates, foreign exchange reserves, the composite stock price index, real exchange rates, real deposit rates, bank deposits, loan and deposit interest rates, the difference between the real BI rate and the real FED rate, the M1, M2 multiplier, and the ratio of M2 to foreign exchange reserves. Meanwhile, the dependent variable uses the perfect signal value based on the Financial Pressure Index. A detailed statistical analysis of the dataset is also given by threshold value to convey crisis conditions. Experimental analysis shows that the proposed model is reliable based on the different evaluation criteria. The case studies show that the result for predictive data is basically consistent with the actual situation, which has greatly helped the prediction of a financial crisis. ","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41462163","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}
Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.4106
B. Hosseini, M. Afshari, M. Alizadeh, A. Afify
The choice of the most suitable statistical distribution for modeling data is very important. Generally, the new distributions are more flexible to model real data that present a high degree of skewness and kurtosis. In this paper, we define a new one-parameter lifetime distribution, so-called weighted-Lindley distribution. Some of its basic properties are investigated. Some classical and Bayesian methods of estimation have been used for estimating its parameter. The behavior of these estimators were investigated by a graphical simulation study. A real data set is analyzed to investigate the flexibility of the new weighted-Lindley distribution.
{"title":"A New Weighted-Lindley Distribution: Properties, Classical and Bayesian Estimation with an Application","authors":"B. Hosseini, M. Afshari, M. Alizadeh, A. Afify","doi":"10.18187/pjsor.v18i4.4106","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.4106","url":null,"abstract":"The choice of the most suitable statistical distribution for modeling data is very important. Generally, the new distributions are more flexible to model real data that present a high degree of skewness and kurtosis. In this paper, we define a new one-parameter lifetime distribution, so-called weighted-Lindley distribution. Some of its basic properties are investigated. Some classical and Bayesian methods of estimation have been used for estimating its parameter. The behavior of these estimators were investigated by a graphical simulation study. A real data set is analyzed to investigate the flexibility of the new weighted-Lindley distribution.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45687830","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}
Pub Date : 2022-12-06DOI: 10.18187/pjsor.v18i4.4059
Muntazir Mehdi, M. Aslam, N. Feroze
Transmuted distributions have been centered of focus for researchers recently due to their flexibility and applicability in statistics. However, the only few contributions have considered estimation for mixture of transmuted lifetime models especially under Bayesian methods has been explored more recently. We have considered the Bayesian estimation of transmuted Lomax mixture model (TLMM) for type-I censored samples. The Bayes estimates (BEs) for informative and non-informative priors. The BEs and posterior risks (PRs) are evaluated using four different loss functions (LFs), two symmetric and two asymmetric, namely the squared error loss function (SELF), precautionary loss function (PLF), weighted balance loss function (WBLF), and general entropy loss function (GELF). Simulations are run using Lindley Approximation method to compare the BEs under various sample sizes and censoring rates. The estimates under informative prior and GELF were found superior to their counterparts. The applicability of the proposed estimates has been illustrated using the analysis of a real data regarding type-I censored failure times of windshields airplanes.
{"title":"Bayesian Estimation of Transmuted Lomax Mixture Model with an Application to Type-I Censored Windshield Data","authors":"Muntazir Mehdi, M. Aslam, N. Feroze","doi":"10.18187/pjsor.v18i4.4059","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.4059","url":null,"abstract":"Transmuted distributions have been centered of focus for researchers recently due to their flexibility and applicability in statistics. However, the only few contributions have considered estimation for mixture of transmuted lifetime models especially under Bayesian methods has been explored more recently. We have considered the Bayesian estimation of transmuted Lomax mixture model (TLMM) for type-I censored samples. The Bayes estimates (BEs) for informative and non-informative priors. The BEs and posterior risks (PRs) are evaluated using four different loss functions (LFs), two symmetric and two asymmetric, namely the squared error loss function (SELF), precautionary loss function (PLF), weighted balance loss function (WBLF), and general entropy loss function (GELF). Simulations are run using Lindley Approximation method to compare the BEs under various sample sizes and censoring rates. The estimates under informative prior and GELF were found superior to their counterparts. The applicability of the proposed estimates has been illustrated using the analysis of a real data regarding type-I censored failure times of windshields airplanes.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47060269","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}
Pub Date : 2022-12-05DOI: 10.18187/pjsor.v18i4.3969
A. Alenazi
The paper considers the class of $f$-divergence regression models as alternatives to parametric regression models for compositional data. The special cases examined in this paper include the Jensen-Shannon, Kullback-Leibler, Hellinger, chi^2 and total variation divergence. Strong advantages of the proposed regression models are a) the absence of parametric assumptions and b) the ability to treat zero values (which commonly occur in practice) naturally. Extensive Monte Carlo simulation studies comparatively assess the performance of the models in terms of bias and an empirical evaluation using real data examining further aspects, such as predictive performance and computational cost. The results reveal that Kullback-Leibler and Jensen-Shannon divergence regression models exhibited high quality performance in multiple directions. Ultimately, penalised versions of the Kullback-Leibler divergence regression are introduced and illustrated using real data rendering this model the optimal model to utilise in practice.
{"title":"f-divergence regression models for compositional data","authors":"A. Alenazi","doi":"10.18187/pjsor.v18i4.3969","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.3969","url":null,"abstract":"The paper considers the class of $f$-divergence regression models as alternatives to parametric regression models for compositional data. The special cases examined in this paper include the Jensen-Shannon, Kullback-Leibler, Hellinger, chi^2 and total variation divergence. Strong advantages of the proposed regression models are a) the absence of parametric assumptions and b) the ability to treat zero values (which commonly occur in practice) naturally. Extensive Monte Carlo simulation studies comparatively assess the performance of the models in terms of bias and an empirical evaluation using real data examining further aspects, such as predictive performance and computational cost. The results reveal that Kullback-Leibler and Jensen-Shannon divergence regression models exhibited high quality performance in multiple directions. Ultimately, penalised versions of the Kullback-Leibler divergence regression are introduced and illustrated using real data rendering this model the optimal model to utilise in practice.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41360111","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}
Pub Date : 2022-12-05DOI: 10.18187/pjsor.v18i4.3600
M. Ibrahim, M. M. Ali, H. Goual, H. Yousof
After studying the mathematical properties of the Double Burr XII model, we present Bayesian and non-Bayesian estimation for its unknown parameters. Also, we constructed a new statistical test for goodness-of-fit in case of complete and censored samples. The modified test is developed based on the Nikulin-Rao-Robson statistic for validation. Simulations are performed for assessing the new test along with nine applications on real data.
{"title":"The Double Burr Type XII Model: Censored and Uncensored Validation Using a New Nikulin-Rao-Robson Goodness-of-Fit Test with Bayesian and Non-Bayesian Estimation Methods","authors":"M. Ibrahim, M. M. Ali, H. Goual, H. Yousof","doi":"10.18187/pjsor.v18i4.3600","DOIUrl":"https://doi.org/10.18187/pjsor.v18i4.3600","url":null,"abstract":"After studying the mathematical properties of the Double Burr XII model, we present Bayesian and non-Bayesian estimation for its unknown parameters. Also, we constructed a new statistical test for goodness-of-fit in case of complete and censored samples. The modified test is developed based on the Nikulin-Rao-Robson statistic for validation. Simulations are performed for assessing the new test along with nine applications on real data.","PeriodicalId":19973,"journal":{"name":"Pakistan Journal of Statistics and Operation Research","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48254989","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}
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