scholarly journals Demographic Seasonality

2019 ◽  
Vol 22 (03) ◽  
pp. 187-194 ◽  
Author(s):  
Johan Fellman
Keyword(s):  
At Risk ◽  
Goodness Of Fit ◽  
Extreme Values ◽  
Demographic Data ◽  
Climatic Conditions ◽  
Data Sets ◽  
Test Statistic ◽  
Population At Risk ◽  
Chi Squared ◽  
Number Of Births

AbstractThe seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as ‘non-flatness’ throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

scholarly journals Studies of the Seasonal Pattern of Multiple Maternities

2017 ◽  
Vol 20 (3) ◽  
pp. 250-256 ◽  
Author(s):  
Johan Fellman
Keyword(s):  
At Risk ◽  
Seasonal Variation ◽  
Goodness Of Fit ◽  
Seasonal Pattern ◽  
Population Data ◽  
Seasonal Fluctuations ◽  
Population At Risk ◽  
Chi Squared ◽  
Birth Data ◽  
Number Of Births

The seasonality of population data has been of great interest in demographic studies. When seasonality is analyzed, the population at risk plays a central role. In a study of the monthly number of births and deaths, the population at risk is the product of the size of the population and the length of the month. Usually, the population can be assumed to be constant, and consequently, the population at risk is proportional to the length of the month. Hence, the number of cases per day has to be analyzed. If one studies the seasonal variation in twin or multiple maternities, the population at risk is the total number of monthly confinements, and the study should be based on the rates of the multiple maternities. Consequently, if one considers monthly twinning rates, the monthly number of birth data is eliminated and one obtains an unaffected seasonality measure of the twin maternities. The strength of the seasonality is measured by a chi-squared test or by the standard deviation. When seasonal models are applied, one must pay special attention to how well the model fits the data. If the goodness of fit is poor, it can erroneously result in a statement that the seasonality is slight, although the observed seasonal fluctuations are marked.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

scholarly journals A Modified Chi-square Type Test Statistic for the Double Burr X Model with Applications to Right Censored Medical and Reliability Data

2021 ◽  
pp. 615-623
Author(s):  
Khaoula Aidi ◽  
Nadeem Shafique Butt ◽  
Mir Masoom Ali ◽  
Mohamed Ibrahim ◽  
Haitham M. Yousof ◽  
...  
Keyword(s):  
Censored Data ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Right Censored Data ◽  
The Right

A new modified version of the Bagdonavičius-Nikulin goodness-of-fit test statistic is presented for validity for the right censor case under the double Burr type X distribution. The maximum likelihood estimation method in censored data case is used and applied. Simulations via the algorithm of Barzilai-Borwein is performed for assessing the right censored estimation method. Another simulation study is presented for testing the null hypothesis under the modified version of the Bagdonavičius and Nikulin goodness-of-fit statistical test. Four right censored data sets are analyzed under the new modified test statistic for checking the distributional validation.

scholarly journals Statistical Analysis of Numerical Preclinical Radiobiological Data

2016 ◽  
Author(s):  
Helene Hill ◽  
Joel H Pitt
Keyword(s):  
Goodness Of Fit ◽  
Statistical Tests ◽  
Numerical Data ◽  
Image Manipulation ◽  
Data Sets ◽  
Scientific Fraud ◽  
Grant Applications ◽  
Chi Squared ◽  
Chance Occurrence ◽  
Vexing Problem

<p><strong>Background: </strong>Scientific fraud is an increasingly vexing problem.&nbsp; Many current programs for fraud detection focus on image manipulation, while techniques for detection based on anomalous patterns that may be discoverable in the underlying numerical data&nbsp; get much less attention, even though these techniques are often easy to apply.&nbsp;</p><p><strong>Methods: </strong>We applied statistical techniques in considering and comparing data sets from ten researchers in one laboratory and three outside investigators to determine whether anomalous patterns in data from a research teaching assistant (RTS) were likely to have occurred by chance. Rightmost digits of values in RTS data sets were not, as expected, uniform; equal pairs of terminal digits occurred at higher than expected frequency (&gt; 10%); and, an unexpectedly large number of data triples commonly produced in such research included values near their means as an element. We applied standard statistical tests (chi-squared goodness of fit, binomial probabilities) to determine the likelihood of the first two anomalous patterns, and developed a new statistical model to test the third.</p><p><strong>&nbsp;Results: </strong>Application of the three tests to various data sets reported by RTS resulted in repeated rejection of the hypotheses (often at p-levels well below 0.001) that anomalous patterns in those data may have occurred by chance.&nbsp; Similar application to data sets from other investigators were entirely consistent with chance occurrence.</p><p><strong>Conclusions: </strong>This analysis emphasizes the importance of access to raw data that form the bases of publications, reports and grant applications in order to evaluate the correctness of the conclusions, and the importance of applying statistical methods to detect anomalous, especially potentially fabricated, numerical results.&nbsp;</p>

scholarly journals OP0146 TENOSYNOVITIS, SYNOVIAL HYPERTROPHY AND FEET BURSITIS ARE USEFUL ULTRASOUND BIOMARKERS FOR PREDICTING ARTHRITIS DEVELOPMENT IN A POPULATION AT-RISK FOR RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 87.2-87
Author(s):  
Y. Kisten ◽  
A. Circiumaru ◽  
M. Loberg ◽  
N. Vivar-Pomiano ◽  
A. Antovic ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
At Risk ◽  
Musculoskeletal Complaints ◽  
Population At Risk ◽  
Synovial Hypertrophy ◽  
Extensor Carpi Ulnaris ◽  
Chi Squared ◽  
Good Potential ◽  
Study Participants ◽  
Mtp Joints

Background:Musculoskeletal ultrasound (MSUS) evaluation of individuals at risk for developing rheumatoid arthritis (RA) having Anti-Citrullinated Protein Antibody (ACPA) positivity and musculoskeletal complaints, may play an important role in the very early detection of RA.Objectives:We aimed to identify which ultrasound markers could predict arthritis development.Methods:Individuals with musculoskeletal complaints with a positive anti-CCP2 test were referred to the rheumatology department for a detailed clinical (68 joint count) and MSUS examination of the hands, feet and any symptomatic joints. Only those without clinical and/or MSUS detected arthritis were included in the RISK RA prospective cohort and followed-up over 3 years/ or until arthritis onset. Using EULAR-OMERACT guidelines1, MSUS markers for synovial hypertrophy (SH) and hyperemia (Doppler activity) were documented for each visit. Finger and wrist tendons were screened for any signs of tenosynovitis (TS), and between metatarsal joints for bursitis. Association of MSUS biomarkers with arthritis development was tested (comparing proportions) using Chi-Squared or Fisher’s exact tests.Results:288 individuals were included from January 2014 to October 2019 (79% female, 35% RF positive, median age 48 years: IQR: 36-58). Within a median of 38 months (IQR: 1-72) since recruitment, 84 individuals (28%) developed an arthritis diagnosis.Prior to obtaining any diagnosis (at inclusion and/or follow-up visit), 95 of the 288 individuals (33%) had at least one type of MSUS anatomical modification present (around the tendons, joint synovium and/or within bursal cavities), and 56% (53/95) of these individuals eventually developed arthritis. Of the remaining 193 that did not present with any obvious MSUS changes, 16% progressed towards arthritis development.The presence of tenosynovitis was detected in 64 of 288 individuals scanned prior to diagnosis and were more frequent in those developing arthritis (44%, 37/84) as compared to those with TS not developing arthritis (13%, 27/204), p<0.0001. The extensor carpi ulnaris wrist tendons were mostly involved. Sonographic changes within the synovium were noted in 11% (32/288) of all individuals, mostly affecting the metacarpophalangeal (MCP) and metatarsophalangeal (MTP) joints. There was a higher incidence of synovial hypertrophy detected in those developing arthritis (22%, 18/24), as compared to those that remained arthritis free (7%, 14/204), p<0.0001. The MCP joints with synovial hypertrophy were more prone to arthritis development as compared to the MTP’s. Furthermore, we observed a higher frequency of bursitis between the MTP joints in individuals developing arthritis, as compared to individuals having a bursitis who did not develop arthritis (13%, 11/84 versus 7%, 14/204, p=0.009).Conclusion:Ultrasound biomarkers such as tenosynovitis of the extensor carpi ulnaris, synovial hypertrophy of the MCP joints and feet bursitis have good potential to predict arthritis development in a population at-risk for rheumatoid arthritis.References:[1]Maria-Antonietta D’Agostino et al. RMD Open 2017;3:e 000428Acknowledgements:All study participants and patients, including researchers that are part of the multidisciplinary laboratory, clinical and academic teams of the RISK RA study group, as well as all assisting this research in one form or the other are greatly acknowledged.Disclosure of Interests:None declared

scholarly journals Validation of the Odd Lindley Exponentiated Exponential by a ModiÖed Goodness of Fit Test with Applications to Censored and Complete Data

2019 ◽  
pp. 745-771 ◽  
Author(s):  
Hafida Goual ◽  
Haitham M. Yousof ◽  
Mir Masoom Ali
Keyword(s):  
Goodness Of Fit ◽  
Maximum Likelihood Estimators ◽  
Real Data ◽  
Complete Data ◽  
Data Sets ◽  
Grouped Data ◽  
Goodness Of Fit Test ◽  
Right Censored Data ◽  
Mathematical Properties ◽  
Chi Squared

In this paper, we Örst introduse a new extension of the exponentiated exponential distribution along with its several mathematical properties. Second, we construct a modiÖed Chi-squared goodness-of-Öt test based on the Nikulin-Rao-Robson statistic in presence of censored and complete data. We describe the theory and the mechanism of the Y 2 n statistic test which can be used in survival and reliability data analysis. We use the maximum likelihood estimators based on the initial non grouped data sets. Then, we conduct numerical simulations to reinforce the results. For showing the applicability of our model in various Öelds, we illustrate it and the proposed test by applications to two real data sets for complete data case and two other right censored data sets.

scholarly journals A Modified Chi-square Type Test for Distributional Validity with Applications to Right Censored Reliability and Medical Data

2021 ◽  
pp. 1113-1121 ◽  
Author(s):  
Haitham M. Yousof ◽  
Abdullah H. Al-nefaie ◽  
Khaoula Aidi ◽  
M. Masoom Ali ◽  
Mohamed ibrahim Mohamed
Keyword(s):  
Censored Data ◽  
Goodness Of Fit ◽  
Data Sets ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Right Censored Data ◽  
Type Test ◽  
The Right ◽  
New Distribution

In this paper, a modified Chi-square goodness-of-fit test called the modified Bagdonavičius-Nikulin goodness-of-fit test statistic is investigated and the applied for distributional validation under the right censored case. The new modified goodness-of-fit test is presented and applied for the right censored data sets. The algorithm of the censored Barzilai-Borwein is employed via a comprehensive simulation study for assessing validity of the new test. The modified Bagdonavičius-Nikulin test is applied to four real and right censored data sets. A new distribution is compared with many other competitive distributions under the new modified Bagdonavičius-Nikulin goodness-of-fit test statistic.

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