Pub Date : 2022-06-01DOI: 10.2478/stattrans-2022-0023
T. Bednarski, Piotr B. Nowak, Magdalena Skolimowska-Kulig
Abstract The Cox proportional hazards model has become the most widely used procedure in survival analysis. The theoretical basis of the original model has been developed in various extensions. In the recent years, vital research has been undertaken involving the incorporation of random effects to survival models. In this setting, the random effect is a variable (frailty) which embraces a variation among individuals or groups of individuals which cannot be explained by observable covariates. The right choice of the frailty distribution is essential for an accurate description of the dependence structure present in the data. In this paper, we aim to investigate the accuracy of inference based on the primer Cox model in the existence of unobserved heterogeneity, that is, when the data generating mechanism is more complex than presumed and described by the kind of an extension of the Cox model with undefined frailty. We show that the conventional partial likelihood estimator under the considered extension is Fisher-consistent up to a scaling factor, provided symmetry-type distributional assumptions on covariates. We also present the results of simulation experiments that reveal an exemplary behaviour of the estimators.
{"title":"Scaled Fisher consistency for the partial likelihood estimation in various extensions of the Cox model","authors":"T. Bednarski, Piotr B. Nowak, Magdalena Skolimowska-Kulig","doi":"10.2478/stattrans-2022-0023","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0023","url":null,"abstract":"Abstract The Cox proportional hazards model has become the most widely used procedure in survival analysis. The theoretical basis of the original model has been developed in various extensions. In the recent years, vital research has been undertaken involving the incorporation of random effects to survival models. In this setting, the random effect is a variable (frailty) which embraces a variation among individuals or groups of individuals which cannot be explained by observable covariates. The right choice of the frailty distribution is essential for an accurate description of the dependence structure present in the data. In this paper, we aim to investigate the accuracy of inference based on the primer Cox model in the existence of unobserved heterogeneity, that is, when the data generating mechanism is more complex than presumed and described by the kind of an extension of the Cox model with undefined frailty. We show that the conventional partial likelihood estimator under the considered extension is Fisher-consistent up to a scaling factor, provided symmetry-type distributional assumptions on covariates. We also present the results of simulation experiments that reveal an exemplary behaviour of the estimators.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"185 - 196"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46189480","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0022
S. Bonnini, Getnet Melak Assegie
Abstract In many applications of the multivariate analyses of variance, the classic parametric solutions for testing hypotheses of equality in population means or multisample and multivariate location problems might not be suitable for various reasons. Multivariate multisample location problems lack a comparative study of the power behaviour of the most important combined permutation tests as the number of variables diverges. In particular, it is useful to know under which conditions each of the different tests is preferable in terms of power, how the power of each test increases when the number of variables under the alternative hypothesis diverges, and the power behaviour of each test as the function of the proportion of true alternative hypotheses. The purpose of this paper is to fill the gap in the literature about combined permutation tests, in particular for big data with a large number of variables. A Monte Carlo simulation study was carried out to investigate the power behaviour of the tests, and the application to a real case study was performed to show the utility of the method.
{"title":"Advances on Permutation Multivariate Analysis of Variance for big data","authors":"S. Bonnini, Getnet Melak Assegie","doi":"10.2478/stattrans-2022-0022","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0022","url":null,"abstract":"Abstract In many applications of the multivariate analyses of variance, the classic parametric solutions for testing hypotheses of equality in population means or multisample and multivariate location problems might not be suitable for various reasons. Multivariate multisample location problems lack a comparative study of the power behaviour of the most important combined permutation tests as the number of variables diverges. In particular, it is useful to know under which conditions each of the different tests is preferable in terms of power, how the power of each test increases when the number of variables under the alternative hypothesis diverges, and the power behaviour of each test as the function of the proportion of true alternative hypotheses. The purpose of this paper is to fill the gap in the literature about combined permutation tests, in particular for big data with a large number of variables. A Monte Carlo simulation study was carried out to investigate the power behaviour of the tests, and the application to a real case study was performed to show the utility of the method.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"163 - 183"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48600894","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0024
Hemlata Joshi, S. Azarudheen, M. Nagaraja, Singh Chandraketu
Abstract The COVID-19 pandemic has recently become a threat all across the globe with the rising cases every day and many countries experiencing its outbreak. According to the WHO, the virus is capable of spreading at an exponential rate across countries, and India is now one of the worst-affected country in the world. Researchers all around the world are racing to come up with a cure or treatment for COVID-19, and this is creating extreme pressure on the policy makers and epidemiologists. However, in India the recovery rate has been far better than in other countries, and is steadily improving. Still in such a difficult situation with no effective medicine, it is essential to know if a patient with the COVID-19 is going to recover or die. To meet this end, a model has been developed in this article to estimate the probability of a recovery of a patient based on the demographic characteristics. The study used data published by the Ministry of Health and Family Welfare of India for the empirical analysis.
{"title":"On the quick estimation of probability of recovery from COVID-19 during first wave of epidemic in India: a logistic regression approach","authors":"Hemlata Joshi, S. Azarudheen, M. Nagaraja, Singh Chandraketu","doi":"10.2478/stattrans-2022-0024","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0024","url":null,"abstract":"Abstract The COVID-19 pandemic has recently become a threat all across the globe with the rising cases every day and many countries experiencing its outbreak. According to the WHO, the virus is capable of spreading at an exponential rate across countries, and India is now one of the worst-affected country in the world. Researchers all around the world are racing to come up with a cure or treatment for COVID-19, and this is creating extreme pressure on the policy makers and epidemiologists. However, in India the recovery rate has been far better than in other countries, and is steadily improving. Still in such a difficult situation with no effective medicine, it is essential to know if a patient with the COVID-19 is going to recover or die. To meet this end, a model has been developed in this article to estimate the probability of a recovery of a patient based on the demographic characteristics. The study used data published by the Ministry of Health and Family Welfare of India for the empirical analysis.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"197 - 208"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44463368","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0016
Sangeeta Arora, K. Mahajan, Vikas Jangra
Abstract Bayesian estimators and highest posterior density credible intervals are obtained for two popular inequality measures, viz. the Gini index and the Bonferroni index in the case of the Dagum distribution. The study considers informative and non-informative priors, i.e. the Mukherjee-Islam prior and the extension of Jeffrey’s prior, respectively, under the presumption of the Linear Exponential (LINEX) loss function. A Monte Carlo simulation study is carried out in order to obtain the relative efficiency of both the Gini and Bonferroni indices while taking into consideration different priors and loss functions. The estimated loss proves lower when using the Mukherjee-Islam prior in comparison to the extension of Jeffrey’s prior and the LINEX loss function outperforms the squared error loss function (SELF) in terms of the estimated loss. Highest posterior density credible intervals are also obtained for both these measures. The study used real-life data sets for illustration purposes.
{"title":"A Bayesian estimation of the Gini index and the Bonferroni index for the Dagum distribution with the application of different priors","authors":"Sangeeta Arora, K. Mahajan, Vikas Jangra","doi":"10.2478/stattrans-2022-0016","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0016","url":null,"abstract":"Abstract Bayesian estimators and highest posterior density credible intervals are obtained for two popular inequality measures, viz. the Gini index and the Bonferroni index in the case of the Dagum distribution. The study considers informative and non-informative priors, i.e. the Mukherjee-Islam prior and the extension of Jeffrey’s prior, respectively, under the presumption of the Linear Exponential (LINEX) loss function. A Monte Carlo simulation study is carried out in order to obtain the relative efficiency of both the Gini and Bonferroni indices while taking into consideration different priors and loss functions. The estimated loss proves lower when using the Mukherjee-Islam prior in comparison to the extension of Jeffrey’s prior and the LINEX loss function outperforms the squared error loss function (SELF) in terms of the estimated loss. Highest posterior density credible intervals are also obtained for both these measures. The study used real-life data sets for illustration purposes.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"49 - 68"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41581835","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0013
A. Mustafa, M. I. Khan
Abstract In this article, the length-biased power hazard rate distribution has introduced and investigated several statistical properties. This distribution reports an extension of several probability distributions, namely: exponential, Rayleigh, Weibull, and linear hazard rate. The procedure of maximum likelihood estimation is taken for parameters. Finally, the applicability of the model is explored by three real data sets. To examine, the performance of the technique, a simulation study is extracted.
{"title":"The length-biased power hazard rate distribution: Some properties and applications","authors":"A. Mustafa, M. I. Khan","doi":"10.2478/stattrans-2022-0013","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0013","url":null,"abstract":"Abstract In this article, the length-biased power hazard rate distribution has introduced and investigated several statistical properties. This distribution reports an extension of several probability distributions, namely: exponential, Rayleigh, Weibull, and linear hazard rate. The procedure of maximum likelihood estimation is taken for parameters. Finally, the applicability of the model is explored by three real data sets. To examine, the performance of the technique, a simulation study is extracted.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"1 - 16"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43633596","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0019
Mateusz Borkowski
Abstract The aim of the research is to identify the strength and direction of the development of the relationship between formal and informal institutions and to assess the institutional equilibrium of modern economies. The structural equations modelling based on partial least squares (SEM-PLS) is applied to achieve the purpose of the article. It is an econometric method that allows the measurement and analysis of the dependencies between latent variables (measures that cannot be directly observed). The study included 27 EU economies and the research period covered the years 2008 and 2018. The results of the study demonstrate that the quality of informal institutions strongly, positively determines the quality of formal institutions. The conducted analyses indicate that modern economies are diversified in terms of the quality of informal and formal institutions and, consequently, in institutional equilibrium. Considerable institutional disparities also translate into a large diversification in economic development. The article proposes a different meaning of institutional equilibrium, understood as the achieved state of institutional structure characterised by high quality informal institutions which interact with each other to improve the efficiency of formal institutions. The article presents a comprehensive model of the institutional structure and a unique method of measuring institutional equilibrium.
{"title":"Institutional equilibrium in EU economies in 2008 and 2018: SEM-PLS models","authors":"Mateusz Borkowski","doi":"10.2478/stattrans-2022-0019","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0019","url":null,"abstract":"Abstract The aim of the research is to identify the strength and direction of the development of the relationship between formal and informal institutions and to assess the institutional equilibrium of modern economies. The structural equations modelling based on partial least squares (SEM-PLS) is applied to achieve the purpose of the article. It is an econometric method that allows the measurement and analysis of the dependencies between latent variables (measures that cannot be directly observed). The study included 27 EU economies and the research period covered the years 2008 and 2018. The results of the study demonstrate that the quality of informal institutions strongly, positively determines the quality of formal institutions. The conducted analyses indicate that modern economies are diversified in terms of the quality of informal and formal institutions and, consequently, in institutional equilibrium. Considerable institutional disparities also translate into a large diversification in economic development. The article proposes a different meaning of institutional equilibrium, understood as the achieved state of institutional structure characterised by high quality informal institutions which interact with each other to improve the efficiency of formal institutions. The article presents a comprehensive model of the institutional structure and a unique method of measuring institutional equilibrium.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"107 - 127"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41885672","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0015
Maciej Jewczak, Magdalena Brudz
Abstract Analyses regarding socio-economic development and quality of life are an important aspect of research and discussion for many international organisations, states and local authorities. Due to the complexity and multidimensionality of these issues, conducting research can be problematic. The conclusions of various analytical centres indicate that there are many paths towards establishing a set of factors which affect quality of life and ways of assessing socio-economic development levels. Depending on the criteria considered, the most common methods for determining the degree of the advancement of life quality or socio-economic development include taxonomical techniques and analyses of potential, which are based mainly on objective data sourced from official registers. The main purpose of the paper is to investigate the level of socio-economic development and quality of life in the European Union in the years 2004 and 2018. The analyses were conducted for a rarely used level of spatial data aggregation, i.e. for NUTS-2 units. The analysis covers only those European regions that were EU members in 2004. As the primary research tool, the two-dimensional development matrix was adopted, which enabled the verification of the hypothesis regarding the convergence of synthetic measures that indicate the levels of socio-economic development and quality of life in the EU regions. For these indices, the development matrix is also used to identify the strengths and weaknesses as well as the opportunities and threats for selected spatial units, and, at the same time, to estimate the rates of change of the socio-economic development and quality of life levels.
{"title":"Socio-economic development and quality of life of NUTS-2 units in the European Union","authors":"Maciej Jewczak, Magdalena Brudz","doi":"10.2478/stattrans-2022-0015","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0015","url":null,"abstract":"Abstract Analyses regarding socio-economic development and quality of life are an important aspect of research and discussion for many international organisations, states and local authorities. Due to the complexity and multidimensionality of these issues, conducting research can be problematic. The conclusions of various analytical centres indicate that there are many paths towards establishing a set of factors which affect quality of life and ways of assessing socio-economic development levels. Depending on the criteria considered, the most common methods for determining the degree of the advancement of life quality or socio-economic development include taxonomical techniques and analyses of potential, which are based mainly on objective data sourced from official registers. The main purpose of the paper is to investigate the level of socio-economic development and quality of life in the European Union in the years 2004 and 2018. The analyses were conducted for a rarely used level of spatial data aggregation, i.e. for NUTS-2 units. The analysis covers only those European regions that were EU members in 2004. As the primary research tool, the two-dimensional development matrix was adopted, which enabled the verification of the hypothesis regarding the convergence of synthetic measures that indicate the levels of socio-economic development and quality of life in the EU regions. For these indices, the development matrix is also used to identify the strengths and weaknesses as well as the opportunities and threats for selected spatial units, and, at the same time, to estimate the rates of change of the socio-economic development and quality of life levels.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"33 - 48"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41984122","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0018
Abdelmalek Gagui, Abdelhak Chouaf
Abstract This paper deals with the conditional hazard estimator of a real response where the variable is given a functional random variable (i.e it takes values in an infinite-dimensional space). Specifically, we focus on the functional index model. This approach offers a good compromise between nonparametric and parametric models. The principle aim is to prove the asymptotic normality of the proposed estimator under general conditions and in cases where the variables satisfy the strong mixing dependency. This was achieved by means of the kernel estimator method, based on a single-index structure. Finally, a simulation of our methodology shows that it is efficient for large sample sizes.
{"title":"On the nonparametric estimation of the conditional hazard estimator in a single functional index","authors":"Abdelmalek Gagui, Abdelhak Chouaf","doi":"10.2478/stattrans-2022-0018","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0018","url":null,"abstract":"Abstract This paper deals with the conditional hazard estimator of a real response where the variable is given a functional random variable (i.e it takes values in an infinite-dimensional space). Specifically, we focus on the functional index model. This approach offers a good compromise between nonparametric and parametric models. The principle aim is to prove the asymptotic normality of the proposed estimator under general conditions and in cases where the variables satisfy the strong mixing dependency. This was achieved by means of the kernel estimator method, based on a single-index structure. Finally, a simulation of our methodology shows that it is efficient for large sample sizes.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"89 - 105"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48262383","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0020
Sakshi Kaushik, A. Sabharwal, G. Grover
Abstract Mental disorders are common non-communicable diseases whose occurrence rises at epidemic rates globally. The determination of the severity of a mental illness has important clinical implications and it serves as a prognostic factor for effective intervention planning and management. This paper aims to identify the relevant predictors of the severity of mental illnesses (measured by psychiatric rating scales) from a wide range of clinical variables consisting of information on both laboratory test results and psychiatric factors. The laboratory test results collectively indicate the measurements of 23 components derived from vital signs and blood tests results for the evaluation of the complete blood count. The 8 psychiatric factors known to affect the severity of mental illnesses are considered, viz. the family history, course and onset of an illness, etc. Retrospective data of 78 patients diagnosed with mental and behavioural disorders were collected from the Lady Hardinge Medical College & Smt. S.K, Hospital in New Delhi, India. The observations missing in the data are imputed using the non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute shrinkage and selection operator (LASSO), viz. adaptive and group LASSO are used for fitting the regression model. Optimal tuning parameter λ is obtained through 13-fold cross-validation. It was observed that the coefficients of the quantitative predictors extracted by the adaptive LASSO and the group of predictors extracted by the group LASSO were comparable to the coefficients obtained through ridge regression.
{"title":"Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models","authors":"Sakshi Kaushik, A. Sabharwal, G. Grover","doi":"10.2478/stattrans-2022-0020","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0020","url":null,"abstract":"Abstract Mental disorders are common non-communicable diseases whose occurrence rises at epidemic rates globally. The determination of the severity of a mental illness has important clinical implications and it serves as a prognostic factor for effective intervention planning and management. This paper aims to identify the relevant predictors of the severity of mental illnesses (measured by psychiatric rating scales) from a wide range of clinical variables consisting of information on both laboratory test results and psychiatric factors. The laboratory test results collectively indicate the measurements of 23 components derived from vital signs and blood tests results for the evaluation of the complete blood count. The 8 psychiatric factors known to affect the severity of mental illnesses are considered, viz. the family history, course and onset of an illness, etc. Retrospective data of 78 patients diagnosed with mental and behavioural disorders were collected from the Lady Hardinge Medical College & Smt. S.K, Hospital in New Delhi, India. The observations missing in the data are imputed using the non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute shrinkage and selection operator (LASSO), viz. adaptive and group LASSO are used for fitting the regression model. Optimal tuning parameter λ is obtained through 13-fold cross-validation. It was observed that the coefficients of the quantitative predictors extracted by the adaptive LASSO and the group of predictors extracted by the group LASSO were comparable to the coefficients obtained through ridge regression.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"129 - 152"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42749561","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 : 2022-06-01DOI: 10.2478/stattrans-2022-0014
Fariha Sohil, M. U. Sohail, J. Shabbir, Sat Gupta
Abstract In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We consider three different situations for the presence of extreme values and also consider jackknife variance estimators for the population variance by handling these extreme values under stratified random sampling. Bootstrap technique ABB is carried out to understand the relative relationship more precisely.
{"title":"Jackknife winsorized variance estimator under imputed data","authors":"Fariha Sohil, M. U. Sohail, J. Shabbir, Sat Gupta","doi":"10.2478/stattrans-2022-0014","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0014","url":null,"abstract":"Abstract In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We consider three different situations for the presence of extreme values and also consider jackknife variance estimators for the population variance by handling these extreme values under stratified random sampling. Bootstrap technique ABB is carried out to understand the relative relationship more precisely.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"17 - 32"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49648591","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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