Pub Date : 2017-01-01DOI: 10.4172/2155-6180.1000383
Chongming Yang, J. Olsen, S. Coyne, Jing Yu
Ordinal scales can be used in latent growth curve modeling in three ways: mean, weighted mean scores, and factors measured by scale items. Sum and mean scores are commonly used in growth curve modeling in spite of certain discouragement. It was unclear how much bias these practices could produce in terms of the change rates and patterns. This study compared three methods with Monte Carlo Simulations under different number of response categories of the items, in terms of five key parameters of growth curve modeling. The hypothetical population models were derived from real empirical data to generate datasets of binary, trichotomous, five- and seven-point scales with sample size of 300. Latent growth curve modeling of mean, weighted mean, and factors measured by the ordinal scales were respectively fit to these datasets. Results indicated that modeling the factors that are measured with ordinal scales yield the fewest biases. Biases of modeling the means and weighted of the scales were under one decimal point in the change rates, whereas biases in the variances and covariance of the intercept and slope factors were large. In conclusion, it is inadvisable to use means or weighted means of ordinal scales for latent growth curve modeling. It produces the best results modeling the factors that are measured with the ordinal scales.
{"title":"Latent Growth Curve Modeling of Ordinal Scales: A Comparison of Three Strategies","authors":"Chongming Yang, J. Olsen, S. Coyne, Jing Yu","doi":"10.4172/2155-6180.1000383","DOIUrl":"https://doi.org/10.4172/2155-6180.1000383","url":null,"abstract":"Ordinal scales can be used in latent growth curve modeling in three ways: mean, weighted mean scores, and factors measured by scale items. Sum and mean scores are commonly used in growth curve modeling in spite of certain discouragement. It was unclear how much bias these practices could produce in terms of the change rates and patterns. This study compared three methods with Monte Carlo Simulations under different number of response categories of the items, in terms of five key parameters of growth curve modeling. The hypothetical population models were derived from real empirical data to generate datasets of binary, trichotomous, five- and seven-point scales with sample size of 300. Latent growth curve modeling of mean, weighted mean, and factors measured by the ordinal scales were respectively fit to these datasets. Results indicated that modeling the factors that are measured with ordinal scales yield the fewest biases. Biases of modeling the means and weighted of the scales were under one decimal point in the change rates, whereas biases in the variances and covariance of the intercept and slope factors were large. In conclusion, it is inadvisable to use means or weighted means of ordinal scales for latent growth curve modeling. It produces the best results modeling the factors that are measured with the ordinal scales.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70291930","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 : 2017-01-01Epub Date: 2017-02-08DOI: 10.4172/2155-6180.1000335
Gebregziabher Mulugeta, Mark A Eckert, Kenneth I Vaden, Timothy D Johnson, Andrew B Lawson
Functional neuroimaging has provided fundamental advances in our understanding of human brain function and is increasingly used clinically for defining atypical function and surgical planning. For example, functional imaging with blood oxygenation level dependent (BOLD) contrast as a response measure is used as a clinical tool for defining atypical development, pathology, surgical planning, and evaluating treatment outcomes. Despite years of statistical advances in the analysis of complete whole brain data, there has been a limited statistical advance to address the pronounced missingness in many functional imaging studies that use large discovery or small clinical case data. For example, functional magnetic resonance imaging (fMRI) analyses do not always include the entire brain due to image acquisition space limitations and susceptibility artifacts (a loss and spatial distortion of signal that results from a disruption in the magnetic field). The consequence is ‘no data’ or ‘bad data’, respectively. No data occurs when the image acquisition doesn’t cover the whole head which leads to no values. In addition to susceptibility artifacts, bad data can occur across the brain because of motion artifacts. Because statistic maps with applied effect size or significance thresholds do not typically include information about which voxels were omitted from analyses, missing data can result in Type II errors for regions that were not tested. Missing data in fMRI studies can therefore undermine the benefits provided by high quality imaging technology used to generate data testing predictions about brain function.
{"title":"Methods for the Analysis of Missing Data in FMRI Studies.","authors":"Gebregziabher Mulugeta, Mark A Eckert, Kenneth I Vaden, Timothy D Johnson, Andrew B Lawson","doi":"10.4172/2155-6180.1000335","DOIUrl":"https://doi.org/10.4172/2155-6180.1000335","url":null,"abstract":"Functional neuroimaging has provided fundamental advances in our understanding of human brain function and is increasingly used clinically for defining atypical function and surgical planning. For example, functional imaging with blood oxygenation level dependent (BOLD) contrast as a response measure is used as a clinical tool for defining atypical development, pathology, surgical planning, and evaluating treatment outcomes. Despite years of statistical advances in the analysis of complete whole brain data, there has been a limited statistical advance to address the pronounced missingness in many functional imaging studies that use large discovery or small clinical case data. For example, functional magnetic resonance imaging (fMRI) analyses do not always include the entire brain due to image acquisition space limitations and susceptibility artifacts (a loss and spatial distortion of signal that results from a disruption in the magnetic field). The consequence is ‘no data’ or ‘bad data’, respectively. No data occurs when the image acquisition doesn’t cover the whole head which leads to no values. In addition to susceptibility artifacts, bad data can occur across the brain because of motion artifacts. Because statistic maps with applied effect size or significance thresholds do not typically include information about which voxels were omitted from analyses, missing data can result in Type II errors for regions that were not tested. Missing data in fMRI studies can therefore undermine the benefits provided by high quality imaging technology used to generate data testing predictions about brain function.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37231794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01DOI: 10.4172/2155-6180.1000379
G. Tsitsiashvili
The problem considered in this paper is discussing last year’s sufficiently intensively in different biological journals [1-3]. In Tsitsiashvili [4] a problem of a decomposition of balance equations for flows intensities in queuing networks is solving. Such problem is connecting with calculation of flows intensities of proteins networks. Main idea of this procedure is to find clusters with cyclically equivalent nodes and order them by their maximal distance from input clusters so that direct edges may be only from clusters with smaller to clusters with larger distances. Therefore, it is possible to divide all nodes into sets A0,A1,...,Ap so that every edge of the graph is only from node i ∈ At to node j ∈ Aq, t
本文考虑的问题是在不同的生物学期刊上对去年的问题进行了充分深入的讨论[1-3]。在Tsitsiashvili b[4]中，求解了排队网络中流强度平衡方程的分解问题。这一问题与蛋白质网络流动强度的计算有关。该过程的主要思想是寻找具有循环等效节点的簇，并根据它们与输入簇的最大距离对它们进行排序，以便直接边只能从较小的簇到较大距离的簇。因此，可以将所有节点划分为集合A0,A1，…，Ap使得图的每条边仅从节点i∈At到节点j∈Aq, t
{"title":"Calculation of Flows Intensities Transformations in Acyclic Directed Networks","authors":"G. Tsitsiashvili","doi":"10.4172/2155-6180.1000379","DOIUrl":"https://doi.org/10.4172/2155-6180.1000379","url":null,"abstract":"The problem considered in this paper is discussing last year’s sufficiently intensively in different biological journals [1-3]. In Tsitsiashvili [4] a problem of a decomposition of balance equations for flows intensities in queuing networks is solving. Such problem is connecting with calculation of flows intensities of proteins networks. Main idea of this procedure is to find clusters with cyclically equivalent nodes and order them by their maximal distance from input clusters so that direct edges may be only from clusters with smaller to clusters with larger distances. Therefore, it is possible to divide all nodes into sets A0,A1,...,Ap so that every edge of the graph is only from node i ∈ At to node j ∈ Aq, t<q. Then a solution of balance equations system is dividing into solutions of sub systems for clusters with cyclically equivalent nodes so that these sub systems may be solving sequentially in accordance with their ordering by maximal distances.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"9 1","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70291796","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 : 2017-01-01DOI: 10.4172/2155-6180.1000371
K. Ch
Biometrics refers to metrics related to human characteristics. Biometrics is a realistic authentication used as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are then measurable, distinctive characteristics used to label and describe individuals. Biometric authenticators are frequently labeled as behavioral as well as physiological characteristics. Physiological characteristics are related to the shape of the body. By utilizing biometrics a man could be distinguished in view of "who she/he is" instead of "what she/he has" (card, token, scratch) or "what she/he knows" (secret key, PIN).In this paper, the fundamental concentrate is on the different biometrics and their applications. Citation: Kalyani CH (2017) Various Biometric Authentication Techniques: A Review. J Biom Biostat 8: 371. doi: 10.4172/2155-6180.1000371
{"title":"Various Biometric Authentication Techniques: A Review","authors":"K. Ch","doi":"10.4172/2155-6180.1000371","DOIUrl":"https://doi.org/10.4172/2155-6180.1000371","url":null,"abstract":"Biometrics refers to metrics related to human characteristics. Biometrics is a realistic authentication used as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are then measurable, distinctive characteristics used to label and describe individuals. Biometric authenticators are frequently labeled as behavioral as well as physiological characteristics. Physiological characteristics are related to the shape of the body. By utilizing biometrics a man could be distinguished in view of \"who she/he is\" instead of \"what she/he has\" (card, token, scratch) or \"what she/he knows\" (secret key, PIN).In this paper, the fundamental concentrate is on the different biometrics and their applications. Citation: Kalyani CH (2017) Various Biometric Authentication Techniques: A Review. J Biom Biostat 8: 371. doi: 10.4172/2155-6180.1000371","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70291708","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 : 2017-01-01DOI: 10.4172/2155-6180.1000385
B. Husowitz, R. Sanchez-Arias
A support vector classification wrapper feature elimination approach was used to find the most relevant pairs of molecular features that adequately and accurately can predict acute aquatic toxicity. These pairs were then used to derive chemical thresholds or boundaries between chemical properties for toxic and nontoxic organic chemicals that can be used as a “rule of thumb” to design less toxic chemicals. The most relevant pairs were determined to be: Lowest Unoccupied Molecular Orbital (LUMO) and Aqueous Solubility (QPlogS), Difference between the LUMO and HOMO (dE) and Octonal-Water Partition Coefficient (QPlogo.w), and Difference between the LUMO and HOMO (dE) and Van der Waals surface area of polar nitrogen and oxygen atoms (PSA). Projected hyper planes were constructed for each pair and the following thresholds were found: for Lowest Unoccupied Molecular Orbital (LUMO) and Aqueous Solubility (QPlogS) they roughly correspond to QPlogS>-1 and LUMO>1, and for Octonal-Water Partition Coefficient (QPlogo.w) vs. difference between the LUMO and HOMO (dE) they roughly correspond to QPlogo.w 9. This study shows how a statistical approach such as support vector machines can be applied to the rational design of chemicals with reduced toxicity.
{"title":"A Machine Learning Approach to Designing Guidelines for Acute Aquatic Toxicity","authors":"B. Husowitz, R. Sanchez-Arias","doi":"10.4172/2155-6180.1000385","DOIUrl":"https://doi.org/10.4172/2155-6180.1000385","url":null,"abstract":"A support vector classification wrapper feature elimination approach was used to find the most relevant pairs of molecular features that adequately and accurately can predict acute aquatic toxicity. These pairs were then used to derive chemical thresholds or boundaries between chemical properties for toxic and nontoxic organic chemicals that can be used as a “rule of thumb” to design less toxic chemicals. The most relevant pairs were determined to be: Lowest Unoccupied Molecular Orbital (LUMO) and Aqueous Solubility (QPlogS), Difference between the LUMO and HOMO (dE) and Octonal-Water Partition Coefficient (QPlogo.w), and Difference between the LUMO and HOMO (dE) and Van der Waals surface area of polar nitrogen and oxygen atoms (PSA). Projected hyper planes were constructed for each pair and the following thresholds were found: for Lowest Unoccupied Molecular Orbital (LUMO) and Aqueous Solubility (QPlogS) they roughly correspond to QPlogS>-1 and LUMO>1, and for Octonal-Water Partition Coefficient (QPlogo.w) vs. difference between the LUMO and HOMO (dE) they roughly correspond to QPlogo.w 9. This study shows how a statistical approach such as support vector machines can be applied to the rational design of chemicals with reduced toxicity.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292060","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 : 2017-01-01DOI: 10.4172/2155-6180.1000386
P. Sultana, M. TahidurRahman, D. Roy
Background: Tobacco use is a manmade manner which causes severe chronic diseases and Bangladesh is one of the most tobacco prevalent countries in the world. Advertisement and promotion events may have a big contribution to accelerate this. Therefore, this study aimed to analyze the advertisement and promotion events that encouraged the tobacco user. Data and methods: Secondary data of sample size 9629 collected by the Global Adult Tobacco Survey (GATS), 2010 has been used. Along with descriptive analysis, binary logistic regression has been used to analyze the sociodemographic and economic correlates to be encouraged by marketing policy. Results: The most common site for noticing cigarette, bidi and smokeless tobacco product advertisements was in stores (49.90%, 26.25% and 13.97%). From logistic regression it has been found that rural respondents are 1.17 times more inspired to smoke (OR=1.17, 95% CI=1.06, 1.30) from marketing policy than urban respondents. Female respondents are less inspired to smoke (OR=0.24, 95% CI=0.20, 0.28) than male respondents. Older respondents are less inspired to smoke by marketing policy than younger respondents (OR=0.98, 95% CI=0.98, 0.99). On the other hand, Rural respondents are 1.15 times more likely to be inspired to use smokeless product than urban respondents (OR=1.15, 95% CI=1.02, 1.31). Female respondents are 0.63 times less inspired to use smokeless tobacco product than male respondents (OR=0.63, 95% CI=0.51, 0.77) by marketing policy. Older respondents are less inspired to use smokeless tobacco products by marketing policy than younger respondents (OR=0.99, 95% CI=0.98, 0.99). Conclusion: To reduce tobacco use in Bangladesh, Government, policy makers and research institutions that are working for reduction of tobacco use should pay attention more on young, student and female to advocate more. Also, Government could take action to limit advertisement in selling store.
{"title":"Marketing Policy that Accelerate Tobacco Use in Bangladesh: A Statistical Investigation","authors":"P. Sultana, M. TahidurRahman, D. Roy","doi":"10.4172/2155-6180.1000386","DOIUrl":"https://doi.org/10.4172/2155-6180.1000386","url":null,"abstract":"Background: Tobacco use is a manmade manner which causes severe chronic diseases and Bangladesh is one of the most tobacco prevalent countries in the world. Advertisement and promotion events may have a big contribution to accelerate this. Therefore, this study aimed to analyze the advertisement and promotion events that encouraged the tobacco user. Data and methods: Secondary data of sample size 9629 collected by the Global Adult Tobacco Survey (GATS), 2010 has been used. Along with descriptive analysis, binary logistic regression has been used to analyze the sociodemographic and economic correlates to be encouraged by marketing policy. Results: The most common site for noticing cigarette, bidi and smokeless tobacco product advertisements was in stores (49.90%, 26.25% and 13.97%). From logistic regression it has been found that rural respondents are 1.17 times more inspired to smoke (OR=1.17, 95% CI=1.06, 1.30) from marketing policy than urban respondents. Female respondents are less inspired to smoke (OR=0.24, 95% CI=0.20, 0.28) than male respondents. Older respondents are less inspired to smoke by marketing policy than younger respondents (OR=0.98, 95% CI=0.98, 0.99). On the other hand, Rural respondents are 1.15 times more likely to be inspired to use smokeless product than urban respondents (OR=1.15, 95% CI=1.02, 1.31). Female respondents are 0.63 times less inspired to use smokeless tobacco product than male respondents (OR=0.63, 95% CI=0.51, 0.77) by marketing policy. Older respondents are less inspired to use smokeless tobacco products by marketing policy than younger respondents (OR=0.99, 95% CI=0.98, 0.99). Conclusion: To reduce tobacco use in Bangladesh, Government, policy makers and research institutions that are working for reduction of tobacco use should pay attention more on young, student and female to advocate more. Also, Government could take action to limit advertisement in selling store.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292171","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 : 2017-01-01DOI: 10.4172/2155-6180.1000370
Weldeyesus Gebreyowhens Berhe
The study was conducted in Erob district eastern zone of Tigray, Northern Ethiopia to determine the relationship between live weight and linear measurements in Maefur goat population under traditional management system. Data on live body weight, linear body measurement and physical body character were collected from randomly selected 600 (297 male and 303 female goats) and categorized into age group of 04-12, 13-18, 19-24, and 24-35 months equivalent with to 0 PPI, 1 PPI, 2 PPI and 3 ≥ PPI with 82, 87, 134 and 297 animals in each age groups, respectively. The data were analyzed using SPSS software 16.0 version. Descriptive statistics, correlation and regression analysis were used. Heart girth was highly correlated to body weight (r=0.97, P<0.01) and used to predict live body weight with regression equations of y1.04 x-(43.3 ± 0.83), R²0.93 for pooled sex and age. The multiple regression equation for prediction of the live body weight was y=(0.74 HG+0.16 BL+0.18 HW) - 42.8, R2=0.95 for pooled sex and age. It was concluded that, there is variability in body measurements across sex and age indicated that these measurements could be exploited in predicting live body weight. Heart girth was the major body trait used to predict live body weight.
{"title":"Relationship and Prediction of Body Weight from Morphometric Traits in Maefur Goat Population in Tigray, Northern Ethiopia","authors":"Weldeyesus Gebreyowhens Berhe","doi":"10.4172/2155-6180.1000370","DOIUrl":"https://doi.org/10.4172/2155-6180.1000370","url":null,"abstract":"The study was conducted in Erob district eastern zone of Tigray, Northern Ethiopia to determine the relationship between live weight and linear measurements in Maefur goat population under traditional management system. Data on live body weight, linear body measurement and physical body character were collected from randomly selected 600 (297 male and 303 female goats) and categorized into age group of 04-12, 13-18, 19-24, and 24-35 months equivalent with to 0 PPI, 1 PPI, 2 PPI and 3 ≥ PPI with 82, 87, 134 and 297 animals in each age groups, respectively. The data were analyzed using SPSS software 16.0 version. Descriptive statistics, correlation and regression analysis were used. Heart girth was highly correlated to body weight (r=0.97, P<0.01) and used to predict live body weight with regression equations of y1.04 x-(43.3 ± 0.83), R²0.93 for pooled sex and age. The multiple regression equation for prediction of the live body weight was y=(0.74 HG+0.16 BL+0.18 HW) - 42.8, R2=0.95 for pooled sex and age. It was concluded that, there is variability in body measurements across sex and age indicated that these measurements could be exploited in predicting live body weight. Heart girth was the major body trait used to predict live body weight.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70291702","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 : 2017-01-01DOI: 10.4172/2155-6180.1000384
S. Arndorfer, I. Zurbenko
Kolmogorov-Zurbenko filters can be utilized in the public health context analyzing mortality data. This paper aims to expand upon the robust methodology of the KZ filters and their many applications. As a low-pass filter the KZ filters are proven to be the optimal means of analysis for non-stationary data such as mortality data which usually contains various underlying signals: seasonality, long-term trend, and short-term fluctuations. As diabetes incidence and prevalence increases, the burden of health care cost increases, thus prompting the need to understand patterns underlying adverse events related to diabetes, such as mortality. Increasing incidence and prevalence of diabetes prompts the need for preventative measures and understanding what environmental factors are related to adverse events as a result of diabetes. Diabetes mortality across time analyzed with non-parametric models has not previously been studied, thus this extension to the KZ filters is utilized as a preliminary analysis to address the gap in knowledge of diabetes mortality in the United States. Non-parametric time series analysis methods identify an 8.5-year long-term trend as well as annual seasonality of diabetes mortality. Spectral and time analysis of diabetes mortality introduces the relationship between solar activity and diabetes mortality, which is quantified utilizing the cross-correlation between diabetes mortality and total solar irradiation. The strong correlation between solar activity and diabetes mortality confirms the environmental role related specifically to diabetes mortality.
{"title":"Time Series Analysis on Diabetes Mortality in the United States, 1999- 2015 by Kolmogorov-Zurbenko Filter","authors":"S. Arndorfer, I. Zurbenko","doi":"10.4172/2155-6180.1000384","DOIUrl":"https://doi.org/10.4172/2155-6180.1000384","url":null,"abstract":"Kolmogorov-Zurbenko filters can be utilized in the public health context analyzing mortality data. This paper aims to expand upon the robust methodology of the KZ filters and their many applications. As a low-pass filter the KZ filters are proven to be the optimal means of analysis for non-stationary data such as mortality data which usually contains various underlying signals: seasonality, long-term trend, and short-term fluctuations. As diabetes incidence and prevalence increases, the burden of health care cost increases, thus prompting the need to understand patterns underlying adverse events related to diabetes, such as mortality. Increasing incidence and prevalence of diabetes prompts the need for preventative measures and understanding what environmental factors are related to adverse events as a result of diabetes. Diabetes mortality across time analyzed with non-parametric models has not previously been studied, thus this extension to the KZ filters is utilized as a preliminary analysis to address the gap in knowledge of diabetes mortality in the United States. Non-parametric time series analysis methods identify an 8.5-year long-term trend as well as annual seasonality of diabetes mortality. Spectral and time analysis of diabetes mortality introduces the relationship between solar activity and diabetes mortality, which is quantified utilizing the cross-correlation between diabetes mortality and total solar irradiation. The strong correlation between solar activity and diabetes mortality confirms the environmental role related specifically to diabetes mortality.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"735 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70291984","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 : 2016-12-20DOI: 10.4172/2155-6180.1000327
A. Michaud
Summary overview of intelligence development in young children coinciding with neocortex verbal areas development by means of mastery of the reading skill and of the state of children literacy development in the world.
幼儿智力发展与新皮层语言区发展的关系:阅读技能的掌握和世界儿童读写能力发展的现状。
{"title":"Intelligence and Early Mastery of the Reading Skill","authors":"A. Michaud","doi":"10.4172/2155-6180.1000327","DOIUrl":"https://doi.org/10.4172/2155-6180.1000327","url":null,"abstract":"Summary overview of intelligence development in young children coinciding with neocortex verbal areas development by means of mastery of the reading skill and of the state of children literacy development in the world.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"60 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2016-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70291692","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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