Proceedings of the 4th International Conference on Statistics: Theory and Applications最新文献

{"title":"Common Misconceptions and Misunderstandings in Magic Cut-Off for Significance: P-Value","authors":"Arzu Baygül Eden, Neslihan Gokmen Inan","doi":"10.11159/icsta22.125","DOIUrl":"https://doi.org/10.11159/icsta22.125","url":null,"abstract":"- The p-value is the most commonly used statistical term to make a decision in medical statistics. It helps the researchers to decide whether the results (which are obtained from a hypothesis test) show statistical significance or not. Most researchers up today prove their decisions based in p-values. Although the p-value is used so commonly by the researchers, they are in high rate misunderstood, miss-or over-interpreted and also wrongly reported. The purpose of this study is to emphasize some of the misconceptions about p-value, and correct their misunderstanding and suggest to researchers a straight way to use p-value. Fisher, who is called as “father of statistics” were not actually the first one, who used the p-value, however he was the first to outline formally the logic behind its use. Fisher's defined for the p value as we use today: it is equal to the probability of a given experimental observation, under a null hypothesis. If this number were smaller than the acceptable threshold, researchers could \"reject\" the null hypothesis as unlikely to be true. The use of a threshold p value as a basis for rejection was called a \"significance test.\" This is important to distinguish from the \"hypothesis test,\" which will be discussed shortly. In our study we stated some of the misconceptions such as obtaining p-values from inappropriate statistical methods, p-values <0.05 shows clinical significance, using always two-sided p-values, a scientific conclusion is always based on “1” p-value, p-value is the probability of null hypothesis is true, format for table of p-values. As a result, some of the common misconceptions are highlighted about p-value. Being aware of these misconceptions, can increase of the quality of studies.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129824796","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}

{"title":"Approximate Bayesian Inference for Educational Attainment Models","authors":"Shuhrah ALghamdi, Nema Dean, L. Evers","doi":"10.11159/icsta22.140","DOIUrl":"https://doi.org/10.11159/icsta22.140","url":null,"abstract":"- The rapidly expanding volume of educational testing data from online assessments has posed a problem for researchers in modern education. Their main goal is to utilise this information in a timely and adaptive manner to infer skills mastery, improving learning facilities and adapting them to individual learners. Over the past few years, a number of static statistical models have been proposed for extracting knowledge about skills mastery from item response data. However, realistic models typically lead to complex, computationally expensive fitting methods such as MCMC. So these methods will not tend to scale well for streaming data and large-scale real-time systems. The main objective of this paper is to develop approximate Bayesian inference based on the Laplace approximation method (LA), which allows faster inference. The LA estimation method's performance for the one-parameter logistic item response theory (IRT) model has been compared with the MCMC method in a simulation study. Based on the results of several comparison criterion methods such as bias, RMSE and Kendell's measurement distance, the performance of the LA is very good in small, moderate, and relatively large sample size settings. The LA approximately estimated abilities results are very close to the actual values and sometimes even better than the estimated abilities resulting from MCMC. In addition, LA resulted in between a 120 to 900 times speedup over MCMC, making it a more practical alternative for large educational testing datasets. test","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130266593","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}

{"title":"On the Estimation of Empirical Copulas and Joint Densities","authors":"Y. Zang","doi":"10.11159/icsta22.156","DOIUrl":"https://doi.org/10.11159/icsta22.156","url":null,"abstract":"Extended Abstract Copulas are principally utilized for modelling dependencies in multivariate distributions. The key idea behind copulas is that the joint distribution of two or more variables can be represented in terms of their marginal distributions and a specific correlation structure. As a measure of dependence, they have for instance found applications in reliability theory, signal processing, geodesy, hydrology and medicine. Results involving empirical bivariate copula densities are discussed in this presentation. In the proposed methodology, kernel density estimates are utilized to determine the marginal distributions. Then, a moment-based approximation technique for estimating a copula density from bivariate observations is introduced. This approach relies on the estimated joint density, the marginal densities and polynomial representations of the inverse marginal distribution functions. The joint density can be determined by multiplying a base density by a bivariate polynomial whose coefficients are obtained from the joint moments of the distributions. The degrees of the bivariate polynomial adjustment will be selected according to a certain goodness-of-fit criterion. The resulting simple representation of this copula density is suitable for reporting purposes or carrying out further algebraic manipulation. A new approach for obtaining an initial copula density estimate from Deheuvels’ empirical copula is also proposed. For a given bivariate data set, Deheuvels’ copula is evaluated and approximated by means of a bivariate least-squares polynomial. On differentiating this polynomial estimate with respect to both variables, one can obtain a preliminary estimate of the copula density function. Additionally, of a joint density function from the support of a density estimate of the standardized vector, which is taken to be a rectangle. turns out the domain of the original distribution can be delimited by a parallelogram once the back transformation is applied. joint density a","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"33 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126091140","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}

{"title":"Development of a Multi Pollutant Model to Assess Air Pollution Association with Human Health Effects","authors":"S. Jarvis, Wesley S. Burr","doi":"10.11159/icsta22.151","DOIUrl":"https://doi.org/10.11159/icsta22.151","url":null,"abstract":"- A number of methodologies have been developed for investigation of the association between human health and exposure to a single pollutant [e.g., 1]. However, as pollutants are correlated, and the joint effect of pollutants is of high interest, work continues on development for multiple pollutant models. In this work, we discuss a method using Thin Plate Splines (TPS) to simultaneously model both PM 2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) and O 3 (ozone) in association with human mortality. The results are compared to effect estimates obtained from single pollutant models. We find similar temporal trends in the estimates, with large movements in both PM 2.5 and O 3 being captured in the TPS estimates. The estimated errors for the TPS method are larger than the individual models combined and produce risks that are comparable but slightly elevated.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132740290","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}

{"title":"Flexible Semiparametric Kernel Estimation with Bayesian Local Bandwidths and Diagnostics for Multivariate Count Data","authors":"Sobom M. Somé, C. C. Kokonendji","doi":"10.11159/icsta22.126","DOIUrl":"https://doi.org/10.11159/icsta22.126","url":null,"abstract":"While parametric models have fulfilled a prominent role in terms of modelling multivariate data, nonparametric kernel smoothings [1] are recently envisaged for count data. Multivariate count data appear in a wide range of fields like environments (e.g., different kinds of plantation), marketing (e.g., purchases of different products) or epidemiology (e.g., different types of a disease). In this paper, we elaborate two flexible semiparametric approaches governed by the multivariate Poisson with nonnegative cross correlations (from the common covariance µ 0 such that µ 0 =0 or µ 0 >0) for estimating multivariate probability mass functions. In the jungle of multivariate count distributions, we used the so-called generalized dispersion index [2] to compare several distributions between them. Our semiparametric method is then developed through expectation-maximisation scheme [3] and maximum likelihood method to estimate the parameters of the correlated and uncorrelated parametric Poisson departures. Also, the multiple binomial kernel with local Bayesian bandwidths [4] is used for the nonparametric part. Practical diagnostic criteria like the empirical integrated squared errors ISE 0 and the logarithm of the weight functions [5] are here opted to select the correct approach according to the data analysis. For the latter","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125758434","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}

{"title":"A Semiparametric Transition Model for Lifetime Drift of Discrete Electrical Parameters in Semiconductor Devices","authors":"Lukas Sommeregger, H. Lewitschnig","doi":"10.11159/icsta22.133","DOIUrl":"https://doi.org/10.11159/icsta22.133","url":null,"abstract":"Extended Abstract In automotive industry, quality and safety are of high importance. Especially with the upcoming development of autonomous vehicles, the topics of predictive health management and estimation of residual useful life have become topics of interest. Semiconductor manufacturers in this area have to guarantee a high standard of quality in shipped devices over their whole lifetime. Electrical parameters of these devices are specified in data sheets and have to be kept within specified limits over the devices’ expected usage time. To simulate the real lifetime, accelerated stress tests are performed on a random sample of parts. During these tests, electrical parameters may drift over time. This is called lifetime drift. To control for lifetime drift, tighter test limits are introduced at production testing. The goal of these limits is to guarantee quality levels in shipped devices while maximizing manufacturers’ yields. The areas between specified limits and test limits are called guard bands. Statistical models for drift calculation and guard banding parameter drift can be used to identify parameters indicating gradual degradation processes and to estimate the expected remaining useful life of the device. Random samples are put to environmental stress tests. In this way, longitudinal data are generated. Several lifetime drift models for continuous parameters have been developed in the past [1], [2]. However, for discrete parameters (logic vectors, bit-flips, counts etc.) these models are not universally applicable. Furthermore, existing models are currently too computationally expensive to monitor parameters in real time in self-driving vehicles. We propose a semiparametric and distribution-free mixed Markov transition model for discrete parameters based on interval estimation of transition probabilities from sparse data. Drift group formation is considered via clustering and mixture modelling. The method assumes homogenous behavior in the distribution of differences between successive readout points and can be extended to cover several types of interpolating behaviours. The guard banding algorithm is performed using efficient matrix multiplication with intelligent warm starts for the two-dimensional integer optimization problem. For the calculation of residual useful life, we propose one model based on interval estimations from quantile regression on the whole sample and further show how to extend the transition Markov chain model into unobserved time periods. The results are verified via simulation studies and compared to adapted state-of-the-art models for continuous parameters.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117109194","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}

{"title":"Association of E-Cigarette Use during Pregnancy with Adverse Birth Outcomes: A Meta-Analysis","authors":"Zhilin Ren, Yi Yao, Jiyan Ma","doi":"10.11159/icsta22.147","DOIUrl":"https://doi.org/10.11159/icsta22.147","url":null,"abstract":"- With the increasing growth of the global e-cigarette market, more and more pregnant women use e-cigarettes as a safe alternative to traditional cigarettes. However, several studies have shown that e-cigarettes damage the human respiratory and cardiovascular systems and that nicotine is a risk factor for fetal development. To determine the association between e-cigarette use during pregnancy and adverse birth outcomes in pregnant women, we conducted a meta-analysis to assess the present evidence's strength. 735 studies were screened through PubMed, Web of Science, and Scopus databases. A total of nine articles met the inclusion criteria of this paper, and six articles were conducted for a meta-analysis after excluding studies with high heterogeneity. The six studies were further divided into two subgroups based on their differences in study types and population samples: one subgroup was three national case-control studies based on the United State Pregnancy Risk Assessment Monitoring System (PRAMS) database, and the other included three small-scale regional studies. The small for gestational age (SGA) was identified as the indicator for assessing the association between e-cigarette use and adverse birth outcomes in pregnant women. We found a significant association of pregnant women using e-cigarettes during pregnancy with SGA (OR = 1.32, 95%CI = 1.03, 1.69). Low heterogeneity of multiple studies (I 2 =32%) and low publication bias as indicated by funnel plots. Our research proves that the risks of e-cigarette use during pregnancy to the unborn child are evident. Therefore, pregnant women need to be advocated to reduce their use of e-cigarettes during pregnancy.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122253472","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}

{"title":"Identifying Safety-Vaccine Association for COVID-19 Vaccines from VAERS","authors":"Jianping Sun","doi":"10.11159/icsta22.148","DOIUrl":"https://doi.org/10.11159/icsta22.148","url":null,"abstract":"- Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety issues in US licensed vaccines. However, the data mining task for VAERS is quite challenging mainly due to the high dimensionality of the data set and complex confounding presented among adverse events and vaccines. In addition, the inconsistent data quality and the rarity of cases and events add more difficulties to the analysis of VAERS data. Tan et. al. (2022+) [1] proposed a 3-step strategy to detect safety associated vaccines by using zero-inflated Poisson regression model with variable selection from VAERS. In this paper, I discuss the safety issue related with COVID-19 vaccines that were reported to VAERS from year 2020 to current with the implementation of a modified version of 3-step strategy.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125511701","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}

{"title":"An efficient Skip Lot Sampling Plan by Variables Based on Taguchi Capability Index Sciences: An Open Problem","authors":"Nabil EL Farme","doi":"10.11159/icsta22.101","DOIUrl":"https://doi.org/10.11159/icsta22.101","url":null,"abstract":"","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127857411","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}

{"title":"Dispersed Methods for Handling Dispersed Count Data","authors":"Kimberly F. Sellers","doi":"10.11159/icsta22.006","DOIUrl":"https://doi.org/10.11159/icsta22.006","url":null,"abstract":"- While the Poisson distribution is a classical statistical model for count data, it hinges on the constraining equi-dispersion property (i.e. that the mean and variance equal). This assumption, however, does not usually hold for real count data; over-dispersion (i.e. when the variance is greater than the mean) is a more common phenomenon for count data, however data under-dispersion has also been prevalent in various settings. It would be more convenient to work with a distribution that can effectively model data (over- or under-) dispersion because it can offer more flexibility (and, thus, more appropriate inference) in the statistical methodology. This talk introduces the Conway-Maxwell-Poisson distribution along with associated statistical methods motivated by this model to better analyze count data under various scenarios (e.g. distributional theory, generalized linear modeling, control chart theory, and count processes). As time permits, this talk will likewise acquaint the audience with available associated tools for statistical computing.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127906898","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}