Pub Date : 2023-11-28DOI: 10.9734/ajpas/2023/v25i3564
K. Dozie, C. C. Ibebuogu
The study discusses decomposition with the additive model of quadratic trend-cycle in time series. Decomposition method is based on fitting a trend curve by some techniques and de-trending the series, using the de-trended series to adequately estimate and investigate the trend parameters, seasonal indices and residual component of the series. The method adopted in this study assumed that the series are arranged in a Buys-Ballot table with m rows and s columns. The study indicates that, the Buys-Ballot technique is computationally simple when compared with other descriptive techniques. The estimates of the quadratic trend-cycle component and seasonal effects are easily computed from periodic and seasonal averages. Hence, the computations are reduce to (hat{a}) = 3.2051, (hat{b}) = , 0.0218 and (hat{c}) = -0.0001. Therefore, the fitted additive decomposition model is (hat{x})t = 3.2051+ 0.0218t - 0.0001t2 + (hat{s})t Under acceptable assumption, the article shows that additive model satisfies ((Sigma^s_{j=1}) s(_j) = 0) as in equation (7). We also consider test for seasonality that admits additive model in this study.
{"title":"Decomposition with the Additive Model Using Buys-Ballot Technique of Quadratic Trend-Cycle Component in Descriptive Time Series Analysis","authors":"K. Dozie, C. C. Ibebuogu","doi":"10.9734/ajpas/2023/v25i3564","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i3564","url":null,"abstract":"The study discusses decomposition with the additive model of quadratic trend-cycle in time series. Decomposition method is based on fitting a trend curve by some techniques and de-trending the series, using the de-trended series to adequately estimate and investigate the trend parameters, seasonal indices and residual component of the series. The method adopted in this study assumed that the series are arranged in a Buys-Ballot table with m rows and s columns. The study indicates that, the Buys-Ballot technique is computationally simple when compared with other descriptive techniques. The estimates of the quadratic trend-cycle component and seasonal effects are easily computed from periodic and seasonal averages. Hence, the computations are reduce to (hat{a}) = 3.2051, (hat{b}) = , 0.0218 and (hat{c}) = -0.0001. Therefore, the fitted additive decomposition model is (hat{x})t = 3.2051+ 0.0218t - 0.0001t2 + (hat{s})t Under acceptable assumption, the article shows that additive model satisfies ((Sigma^s_{j=1}) s(_j) = 0) as in equation (7). We also consider test for seasonality that admits additive model in this study.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139225955","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 : 2023-11-24DOI: 10.9734/ajpas/2023/v25i3563
M. Raza, Mark Broom
There are strong survival analysis methodologies for data sets which are complete, with accurate information on censoring. But what if they are not complete? In an earlier paper we built a methodology for estimating survival probabilities and hazard functions in a health setting, using breast cancer data from the Kurdistan region of Iraq, for censored and uncensored data when a substantial portion of individuals are lost to the study. In this paper we build on these models to consider further issues based upon the accuracy of the records of patient death, where deaths often occur beyond the hospital in family settings and patients ceasing treatment and contact with the hospital may or may not represent their death; thus the record of their time of death may not be accurate. We develop a new Markov chain-based methodology for generating survival curves and hazard functions, and demonstrate this using a different breast cancer dataset from the Kurdistan region of Iraq.
{"title":"The Use of Survival Analysis Modelling with Incomplete Data with Application to Breast Cancer","authors":"M. Raza, Mark Broom","doi":"10.9734/ajpas/2023/v25i3563","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i3563","url":null,"abstract":"There are strong survival analysis methodologies for data sets which are complete, with accurate information on censoring. But what if they are not complete? In an earlier paper we built a methodology for estimating survival probabilities and hazard functions in a health setting, using breast cancer data from the Kurdistan region of Iraq, for censored and uncensored data when a substantial portion of individuals are lost to the study. In this paper we build on these models to consider further issues based upon the accuracy of the records of patient death, where deaths often occur beyond the hospital in family settings and patients ceasing treatment and contact with the hospital may or may not represent their death; thus the record of their time of death may not be accurate. We develop a new Markov chain-based methodology for generating survival curves and hazard functions, and demonstrate this using a different breast cancer dataset from the Kurdistan region of Iraq.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139240953","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 : 2023-11-17DOI: 10.9734/ajpas/2023/v25i3562
Christiana I. Ezeilo, Onyeagu Sidney I., E. Umeh, C. K. Onyekwere
In this study, we introduce the "Power Chris-Jerry" distribution, conducting a comprehensive analysis of its fundamental mathematical characteristics and an extensive exploration of various crucial aspects. These encompass investigations into its mode, quantile function, moments, coefficient of skewness, kurtosis, moment generating function, stochastic ordering, distribution of order statistics, reliability analysis, and mean past lifetime. Furthermore, we provide an in-depth assessment of four distinct parameter estimation methodologies: maximum likelihood estimation (MLE), Least Squares (LS), maximum product spacing method (MPS), and the Method of Cram`er-von-Mises (CVM). Our investigation uncovers a consistent pattern wherein the MLE, LS, and CVM approaches consistently yield underestimated parameter values. Intriguingly, we observe a consistent trend of decreasing Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and BIAS across all estimation techniques as sample sizes increase. Remarkably, our simulation results consistently favor the Maximum Product Spacing (MPS) method, highlighting its superiority in generating estimates with smaller MSE values across a broad spectrum of parameter values and sample sizes. These findings emphasize the robustness and dependability of the MPS estimator, offering valuable insights and practical guidance for both practitioners and researchers engaged in probability distribution modeling.
{"title":"On Power Chris-Jerry Distribution: Properties and Parameter Estimation Methods","authors":"Christiana I. Ezeilo, Onyeagu Sidney I., E. Umeh, C. K. Onyekwere","doi":"10.9734/ajpas/2023/v25i3562","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i3562","url":null,"abstract":"In this study, we introduce the \"Power Chris-Jerry\" distribution, conducting a comprehensive analysis of its fundamental mathematical characteristics and an extensive exploration of various crucial aspects. These encompass investigations into its mode, quantile function, moments, coefficient of skewness, kurtosis, moment generating function, stochastic ordering, distribution of order statistics, reliability analysis, and mean past lifetime. Furthermore, we provide an in-depth assessment of four distinct parameter estimation methodologies: maximum likelihood estimation (MLE), Least Squares (LS), maximum product spacing method (MPS), and the Method of Cram`er-von-Mises (CVM). Our investigation uncovers a consistent pattern wherein the MLE, LS, and CVM approaches consistently yield underestimated parameter values. Intriguingly, we observe a consistent trend of decreasing Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and BIAS across all estimation techniques as sample sizes increase. Remarkably, our simulation results consistently favor the Maximum Product Spacing (MPS) method, highlighting its superiority in generating estimates with smaller MSE values across a broad spectrum of parameter values and sample sizes. These findings emphasize the robustness and dependability of the MPS estimator, offering valuable insights and practical guidance for both practitioners and researchers engaged in probability distribution modeling.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139265947","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}
Generalized additive model was used to analyse data from Nigeria standard demographic and health survey (NDHS) 2018. The sample consists of 10609 children aged 6-59 months who were tested for malaria parasitemia through the rapid diagnostic test (RDT). Child mortality data was obtained by calculating the difference between the number of children ever born and the proportion of children alive during the survey. The analysis was carried out in R version 4.1.1 via mgcv package. The results obtained indicated linear and nonlinear effects of malaria risk factors on child mortality. The findings also revealed mosquito bed net usage, wealth index, maternal education, type of place of residence and malaria test outcome as significant predictors of child malaria mortality.
{"title":"Modelling the Linear and Nonlinear Effects of Malaria Risk Factors on Child Mortality","authors":"Garba Sahabi Adamu, Gerald Ikechukwu Onwuka, Babayemi Afolabi Wasiu","doi":"10.9734/ajpas/2023/v25i3561","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i3561","url":null,"abstract":"Generalized additive model was used to analyse data from Nigeria standard demographic and health survey (NDHS) 2018. The sample consists of 10609 children aged 6-59 months who were tested for malaria parasitemia through the rapid diagnostic test (RDT). Child mortality data was obtained by calculating the difference between the number of children ever born and the proportion of children alive during the survey. The analysis was carried out in R version 4.1.1 via mgcv package. The results obtained indicated linear and nonlinear effects of malaria risk factors on child mortality. The findings also revealed mosquito bed net usage, wealth index, maternal education, type of place of residence and malaria test outcome as significant predictors of child malaria mortality.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954459","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 : 2023-11-08DOI: 10.9734/ajpas/2023/v25i3560
Elisha J. Inyang, Ette H. Etuk, Ngia M. Nafo, Yvonne A. Da-Wariboko
The battered Nigerian economy has slipped into its second economic recession in five years due to the fallout of the coronavirus pandemic. And this has had a remarkable effect on the value of the Nigerian Naira that are exchanged for a unit of many other currencies of the world. Intervention modelling is used to assess the impact of this external event on the Pakistan Rupee to the Nigerian Naira exchange rates. The dataset for this study is the daily Nigerian Naira exchange rate with respect to the Pakistan Rupee from January–December 2020. The intervention point is marked on April 10, 2020, as a pulse function for the PKR/NGN series. Results revealed that the economic recession due to the fallout of the coronavirus pandemic increased the value of the Nigerian Naira by 3.38% against the Pakistan Rupee, which 1PKR is exchanged for 2.2042NGN compared to the periods before and after the intervention occurred. The intervention was felt at the point of intervention itself but the effect dies immediately after the intervention. Hence, the intervention response is described as an abrupt start and abrupt decay.
{"title":"Time Series Intervention Modelling Based on ESM and ARIMA Models: Daily Pakistan Rupee/Nigerian Naira Exchange Rate","authors":"Elisha J. Inyang, Ette H. Etuk, Ngia M. Nafo, Yvonne A. Da-Wariboko","doi":"10.9734/ajpas/2023/v25i3560","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i3560","url":null,"abstract":"The battered Nigerian economy has slipped into its second economic recession in five years due to the fallout of the coronavirus pandemic. And this has had a remarkable effect on the value of the Nigerian Naira that are exchanged for a unit of many other currencies of the world. Intervention modelling is used to assess the impact of this external event on the Pakistan Rupee to the Nigerian Naira exchange rates. The dataset for this study is the daily Nigerian Naira exchange rate with respect to the Pakistan Rupee from January–December 2020. The intervention point is marked on April 10, 2020, as a pulse function for the PKR/NGN series. Results revealed that the economic recession due to the fallout of the coronavirus pandemic increased the value of the Nigerian Naira by 3.38% against the Pakistan Rupee, which 1PKR is exchanged for 2.2042NGN compared to the periods before and after the intervention occurred. The intervention was felt at the point of intervention itself but the effect dies immediately after the intervention. Hence, the intervention response is described as an abrupt start and abrupt decay.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135430676","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 : 2023-11-06DOI: 10.9734/ajpas/2023/v25i2559
Oliver Mukweyi Pyoko, Renson Muchiri
Despite the availability issue, debt financing continues to be an essential form of funding for businesses. Risks have been a major source of uneasiness for owners, executives, experts, as well as shareholders globally. The Kenyan enterprises have a greater susceptible to variations in currency rates in the nation’s economic climate, which is growing to become increasingly open with an increase in global trade. The study objective is to investigate the effect of risk on total debt of companies listed on Nairobi Securities Exchange. The study was underpinned by tradeoff theory and pecking order theory. The study utilized causal research design. Secondary data was used to collect data from yearly accounting statement from 2007-2011. Panel regression was used to analyze the fixed effect model. The result showed that risk negatively and substantially affects total debt. The study recommended that the management of listed firms should understand the tradeoff theory and pecking order theory. The study also recommended that risk should continually be monitored by companies to be in line with the prevailing economic conditions. This can be ensured by studying other factors trend that can affect the risk of companies.
{"title":"Effect of Risk on Total Debt of Companies Listed on the Nairobi Securities Exchange, Kenya","authors":"Oliver Mukweyi Pyoko, Renson Muchiri","doi":"10.9734/ajpas/2023/v25i2559","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i2559","url":null,"abstract":"Despite the availability issue, debt financing continues to be an essential form of funding for businesses. Risks have been a major source of uneasiness for owners, executives, experts, as well as shareholders globally. The Kenyan enterprises have a greater susceptible to variations in currency rates in the nation’s economic climate, which is growing to become increasingly open with an increase in global trade. The study objective is to investigate the effect of risk on total debt of companies listed on Nairobi Securities Exchange. The study was underpinned by tradeoff theory and pecking order theory. The study utilized causal research design. Secondary data was used to collect data from yearly accounting statement from 2007-2011. Panel regression was used to analyze the fixed effect model. The result showed that risk negatively and substantially affects total debt. The study recommended that the management of listed firms should understand the tradeoff theory and pecking order theory. The study also recommended that risk should continually be monitored by companies to be in line with the prevailing economic conditions. This can be ensured by studying other factors trend that can affect the risk of companies.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634189","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 : 2023-11-04DOI: 10.9734/ajpas/2023/v25i2557
Nafisat Yusuf, Bannister Jerry Zachary
Aims: The aim of this study is to investigate the impact of thresholds on the detection of outliers by comparing the performance of two estimators, namely the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD), at different sample sizes. The study uses simulated data generated from the standard normal distribution to assess how varying thresholds affect the ability of these estimators to detect outliers.
Study Design: This study employs a quantitative research design. It involves the generation of simulated data, the application of the MCD and MRCD estimators for outlier detection, and the systematic manipulation of thresholds and sample size as independent variables.
Place and Duration: The study is conducted using computational tools and did not require a physical location.
Methodology: Simulated data is generated from the standard normal distribution to create a controlled environment for outlier detection experiments. The MCD and MRCD estimators are applied to the simulated data to detect outliers. These estimators are sensitive to deviations from the norm in the data. Different thresholds are systematically applied to the data, and the performance of the estimators is assessed at each threshold level. Thresholds may vary in their extremeness. The study investigates the impact of different sample sizes on outlier detection. This involves using datasets with varying numbers of observations. The r programming language and associated packages are used as the statistical tool for data generation, analysis, and visualization.
Results: The study's findings indicate that the choice of thresholds in data analysis significantly affects the performance of the MCD and MRCD estimators in outlier detection. If the thresholds used for both estimators are the same, their performance is similar. However, differences emerge when thresholds differ from each other. Higher thresholds are shown to identify less extreme outliers, while lower thresholds are effective at identifying more extreme outliers. These results provide insights into the behavior of these estimators in outlier detection scenarios, shedding light on their sensitivity to threshold choices and sample size.Conclusion: Our study has shed light on the critical interdependencies among threshold choices, sample sizes, and the performance of the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD) estimators in the context of outlier detection. By conducting a systematic exploration in a controlled environment with simulated data, we have gleaned valuable insights that can inform both researchers and practitioners in the field of organizational science research.
{"title":"Threshold Effects on Outlier Detection: A Comparative Study of MCD and MRCD Estimators in Multivariate Data Analysis","authors":"Nafisat Yusuf, Bannister Jerry Zachary","doi":"10.9734/ajpas/2023/v25i2557","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i2557","url":null,"abstract":"Aims: The aim of this study is to investigate the impact of thresholds on the detection of outliers by comparing the performance of two estimators, namely the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD), at different sample sizes. The study uses simulated data generated from the standard normal distribution to assess how varying thresholds affect the ability of these estimators to detect outliers.
 Study Design: This study employs a quantitative research design. It involves the generation of simulated data, the application of the MCD and MRCD estimators for outlier detection, and the systematic manipulation of thresholds and sample size as independent variables.
 Place and Duration: The study is conducted using computational tools and did not require a physical location.
 Methodology: Simulated data is generated from the standard normal distribution to create a controlled environment for outlier detection experiments. The MCD and MRCD estimators are applied to the simulated data to detect outliers. These estimators are sensitive to deviations from the norm in the data. Different thresholds are systematically applied to the data, and the performance of the estimators is assessed at each threshold level. Thresholds may vary in their extremeness. The study investigates the impact of different sample sizes on outlier detection. This involves using datasets with varying numbers of observations. The r programming language and associated packages are used as the statistical tool for data generation, analysis, and visualization.
 Results: The study's findings indicate that the choice of thresholds in data analysis significantly affects the performance of the MCD and MRCD estimators in outlier detection. If the thresholds used for both estimators are the same, their performance is similar. However, differences emerge when thresholds differ from each other. Higher thresholds are shown to identify less extreme outliers, while lower thresholds are effective at identifying more extreme outliers. These results provide insights into the behavior of these estimators in outlier detection scenarios, shedding light on their sensitivity to threshold choices and sample size.Conclusion: Our study has shed light on the critical interdependencies among threshold choices, sample sizes, and the performance of the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD) estimators in the context of outlier detection. By conducting a systematic exploration in a controlled environment with simulated data, we have gleaned valuable insights that can inform both researchers and practitioners in the field of organizational science research.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774741","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 : 2023-11-04DOI: 10.9734/ajpas/2023/v25i2558
David Ngmenbelle, Michael Fosu Ofori, Michael Arthur Ofori, Terah Antwi
Background: Breast lumps or lumpiness are a prevalent issue among women seeking guidance, with 40% to 70% reporting lumps or lumpiness. Any woman, regardless of age, who discovers a breast lump by self-examination, screening, or medical intervention begins to worry about developing breast cancer. Late stage of reporting suspected lumps is on the rise and this was impacted by the pandemic. The study examined factors that are associated breast lump and the risk on women who ever had breast lump.
Method: An institutional-based cross-sectional study was conducted on women who attended Peace and Love Hospital in Kumasi, Ghana for breast care services from January to February 2022. Closed-ended questionnaire was used to solicit information from 301 women within a period of six weeks. Chi-square and binary logistic regression model was used to determine the association and the risk respectively.
Results: Breast lump was dominant in women between 41 – 50 years and in those who do not have family history of breast cancer. The findings reveal that educational level [χ2 = 11.170; p = 0.011] and the practice of breast self-examination [χ2 = 7.998; p = 0.005] were significantly associated with breast lump. Married women were 0.764 less likely to have breast lump than those who are singles. Women between 31-40 years were 2 times more likely [AOR=2.061, CI=0.876-4.846] and those between 41-50 years 1 time more likely [AOR=1.131,CI=0.451-2.837] to have breast lump than women between 18 – 30 years.
Conclusion: Breast lump is predominant in women between 31 – 50 years. Factors associated with a woman having breast lump are educational background and the practice of breast self-examination. Surgeon managing a breast lump in women over 30 years old are encouraged to be extremely suspicious and cautious in order to detect and treat malignant lumps early.
{"title":"Factors Associated with Women having Breast Lump in Ghana: A Cross-Sectional Study","authors":"David Ngmenbelle, Michael Fosu Ofori, Michael Arthur Ofori, Terah Antwi","doi":"10.9734/ajpas/2023/v25i2558","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i2558","url":null,"abstract":"Background: Breast lumps or lumpiness are a prevalent issue among women seeking guidance, with 40% to 70% reporting lumps or lumpiness. Any woman, regardless of age, who discovers a breast lump by self-examination, screening, or medical intervention begins to worry about developing breast cancer. Late stage of reporting suspected lumps is on the rise and this was impacted by the pandemic. The study examined factors that are associated breast lump and the risk on women who ever had breast lump.
 Method: An institutional-based cross-sectional study was conducted on women who attended Peace and Love Hospital in Kumasi, Ghana for breast care services from January to February 2022. Closed-ended questionnaire was used to solicit information from 301 women within a period of six weeks. Chi-square and binary logistic regression model was used to determine the association and the risk respectively.
 Results: Breast lump was dominant in women between 41 – 50 years and in those who do not have family history of breast cancer. The findings reveal that educational level [χ2 = 11.170; p = 0.011] and the practice of breast self-examination [χ2 = 7.998; p = 0.005] were significantly associated with breast lump. Married women were 0.764 less likely to have breast lump than those who are singles. Women between 31-40 years were 2 times more likely [AOR=2.061, CI=0.876-4.846] and those between 41-50 years 1 time more likely [AOR=1.131,CI=0.451-2.837] to have breast lump than women between 18 – 30 years. 
 Conclusion: Breast lump is predominant in women between 31 – 50 years. Factors associated with a woman having breast lump are educational background and the practice of breast self-examination. Surgeon managing a breast lump in women over 30 years old are encouraged to be extremely suspicious and cautious in order to detect and treat malignant lumps early.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774690","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 : 2023-11-01DOI: 10.9734/ajpas/2023/v25i2556
A. Audu, U. Usman, S. B. Mohammad, O. A. Joseph
The use of estimators in statistics, quality assurance, and survey methodology can never be over flogged just as the use of sampling. Two of the major challenges of statisticians or surveyors due encounter at the course of data collection in the field of medical and social sciences is non-response and measurement errors. This poses serious problem during data compilation, computation and estimation stages. In this paper, a robust-based classes of estimators are proposed in the presence of non-response and measurement errors through the use of imputation scheme incorporated with measurement errors parameters. The properties of the proposed estimators (Biases & MSES) were derived up to the second degree approximation using Taylors’s series approach. The conditions for the efficiencies of the proposed estimators over the existing estimators was also considered and established in this research. The empirical study conducted using simulated data from normal distribution, exponential distribution, chi-square distribution, uniform distribution, gamma distribution and poison distribution revealed that the modified classes of estimators of the proposed imputation schemes are more efficient and satisfactory than the compared existing estimators. Thus, the proposed modified classes of estimators under imputation scheme were recommended for use in the real life situation especially in the presence of non-response and measurement errors during data analysis and estimation stages.
{"title":"Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error","authors":"A. Audu, U. Usman, S. B. Mohammad, O. A. Joseph","doi":"10.9734/ajpas/2023/v25i2556","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i2556","url":null,"abstract":"The use of estimators in statistics, quality assurance, and survey methodology can never be over flogged just as the use of sampling. Two of the major challenges of statisticians or surveyors due encounter at the course of data collection in the field of medical and social sciences is non-response and measurement errors. This poses serious problem during data compilation, computation and estimation stages. In this paper, a robust-based classes of estimators are proposed in the presence of non-response and measurement errors through the use of imputation scheme incorporated with measurement errors parameters. The properties of the proposed estimators (Biases & MSES) were derived up to the second degree approximation using Taylors’s series approach. The conditions for the efficiencies of the proposed estimators over the existing estimators was also considered and established in this research. The empirical study conducted using simulated data from normal distribution, exponential distribution, chi-square distribution, uniform distribution, gamma distribution and poison distribution revealed that the modified classes of estimators of the proposed imputation schemes are more efficient and satisfactory than the compared existing estimators. Thus, the proposed modified classes of estimators under imputation scheme were recommended for use in the real life situation especially in the presence of non-response and measurement errors during data analysis and estimation stages.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271694","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 : 2023-10-28DOI: 10.9734/ajpas/2023/v25i2555
Olawale Basheer Akanbi
Numerous fields of endeavour have benefited greatly from statistical forecasting, which has aided decision-making by planners and policy makers. In this study, Bayesian Model Averaging (BMA) and Dynamic Model Averaging (DMA) are employed to forecast oil prices in Nigeria. It aimed at predicting the oil prices in Nigeria. Essentially, there are lot of model uncertainties in empirical growth researches. The predictive performance value considering the Mean Squared Forecast Error (MSFE) for BMA and DMA were 920.23 & 540.40 respectively. The DMA predicted the model better than the BMA. High levels of model uncertainties were indeed accounted for, in conformity with the theoretical knowledge.
{"title":"Enhancing Nigerian Oil Price Forecasting: A Comprehensive Analysis of Model Averaging Techniques","authors":"Olawale Basheer Akanbi","doi":"10.9734/ajpas/2023/v25i2555","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v25i2555","url":null,"abstract":"Numerous fields of endeavour have benefited greatly from statistical forecasting, which has aided decision-making by planners and policy makers. In this study, Bayesian Model Averaging (BMA) and Dynamic Model Averaging (DMA) are employed to forecast oil prices in Nigeria. It aimed at predicting the oil prices in Nigeria. Essentially, there are lot of model uncertainties in empirical growth researches. The predictive performance value considering the Mean Squared Forecast Error (MSFE) for BMA and DMA were 920.23 & 540.40 respectively. The DMA predicted the model better than the BMA. High levels of model uncertainties were indeed accounted for, in conformity with the theoretical knowledge.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136160010","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：