• Title/Summary/Keyword: statistical inferences

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Approximate confidence intervals about quantiles in the generalized gamma distribution (일반화 감마분포의 백분위수에 대한 근사신뢰구간)

  • 나종화
    • The Korean Journal of Applied Statistics
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    • v.6 no.2
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    • pp.435-442
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    • 1993
  • For the generalized gamma distribution, exact inferences about quantiles need many computations involving complicated numerical integrations. This paper suggests approximate confidence intervals which are easily obtained by considering the alternative location-scale model. Also, these intervals are very accurate even for small sample size. Approximate confidence intervals about quantiles in the lognormal distribution are also considered. With type 2 censoring data, approximate confidence intervals can also be obtained directly by the suggested methods.

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Likelihood-Based Inference of Random Effects and Application in Logistic Regression (우도에 기반한 임의효과에 대한 추론과 로지스틱 회귀모형에서의 응용)

  • Kim, Gwangsu
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.269-279
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    • 2015
  • This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.

Torsional parameters importance in the structural response of multiscale asymmetric-plan buildings

  • Bakas, Nikolaos;Makridakis, Spyros;Papadrakakis, Manolis
    • Coupled systems mechanics
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    • v.6 no.1
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    • pp.55-74
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    • 2017
  • The evaluation of torsional effects on multistory buildings remains an open issue, despite considerable research efforts and numerous publications. In this study, a large number of multiple test structures are considered with normally distributed topological attributes, in order to quantify the statistically derived relationships between the torsional criteria and response parameters. The linear regression analysis results, depict that the center of twist and the ratio of torsion (ROT) index proved numerically to be the most reliable criteria for the prediction of the modal rotation and displacements, however the residuals distribution and R-squared derived for the ductility demands prediction, was not constant and low respectively. Thus, the assessment of the torsional parameters' contribution to the nonlinear structural response was investigated using artificial neural networks. Utilizing the connection weights approach, the Center of Strength, Torsional Stiffness and the Base Shear Torque curves were found to exhibit the highest impact numerically, while all the other torsional indices' contribution was investigated and quantified.

Statistical Qualitative Analysis on Chemical Mechanical Polishing Process and Equipment Characterization

  • Hong, Sang-Jeen;Hwang, Jong-Ha;Seo, Dong-Sun
    • Transactions on Electrical and Electronic Materials
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    • v.12 no.2
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    • pp.56-59
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    • 2011
  • The characterization of the chemical mechanical polishing (CMP) process for undensified phophosilicate glass (PSG) film is reported using design of experiments (DOE). DOE has been used by experimenters to understand the relationship between the input variables and responses of interest in a simple and efficient way, and it typically is beneficial for determining the appropriatesize of experiments with multiple process variables and making statistical inferences for the responses of interest. The equipment controllable parameters used to operate the machine consist of the down force of the wafer carrier, pressure on the back side wafer, table and spindle speeds (SS), slurry flow (SF) rate, pad condition, etc. None of these are independent ofeach other and, thus, the interaction between the parameters also needs to be understoodfor improved process characterization in CMP. In this study, we selected the five controllable equipment parameters the most recommendedby process engineers, viz. the down force (DF), back pressure (BP), table speed (TS), SS, and SF, for the characterization of the CMP process with respect to the material removal rate and film uniformity in percentage terms. The polished material is undensified PSG which is widely used for the plananization of multi-layered metal interconnects. By statistical modeling and the analysis of the metrology data acquired from a series of $2^{5-1}$ fractional factorial designs with two center points, we showed that the DF, BP and TS have the greatest effect on both the removal rate and film uniformity, as expected. It is revealed that the film uniformity of the polished PSG film contains two and three-way interactions. Therefore, one can easily infer that process control based on a better understanding of the process is the key to success in current semiconductor manufacturing, in which the size of the wafer is approaching 300 mm and is scheduled to continuously increase up to 450 mm in or slightly after 2012.

Model-Based Survival Estimates of Female Breast Cancer Data

  • Khan, Hafiz Mohammad Rafiqullah;Saxena, Anshul;Gabbidon, Kemesha;Rana, Sagar;Ahmed, Nasar Uddin
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.6
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    • pp.2893-2900
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    • 2014
  • Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

Analysis of massive data in astronomy (천문학에서의 대용량 자료 분석)

  • Shin, Min-Su
    • The Korean Journal of Applied Statistics
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    • v.29 no.6
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    • pp.1107-1116
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    • 2016
  • Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

Statistical Qualitative Analysis on Chemical Mechanical Polishing Process and Equipment Characterization

  • Hong, Sang-Jeen;Hwang, Jong-Ha;Seo, Dong-Sun
    • Transactions on Electrical and Electronic Materials
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    • v.12 no.3
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    • pp.115-118
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    • 2011
  • Process characterization of the chemical mechanical polishing (CMP) process for undensified phosphosilicate glass (PSG) film is reported using design of experiments (DOE). DOE has been addressed to experimenters to understand the relationship between input variables and responses of interest in a simple and efficient way. It is typically beneficial for determining the adequate size of experiments with multiple process variables and making statistical inferences for the responses of interests. Equipment controllable parameters to operate the machine include the down force (DF) of the wafer carrier, pressure on the backside of the wafer, table and spindle speed (SS), slurry flow rate, and pad condition. None of them is independent; thus, the interaction between parameters also needs to be indicated to improve process characterization in CMP. In this paper, we have selected the five controllable equipment parameters, such as DF, back pressure (BP), table speed (TS), SS, and slurry flow (SF), most process engineers recommend to characterize the CMP process with respect to material removal rate (RR) and film uniformity as a percentage. The polished material is undensified PSG. PSG is widely used for the plananization in multi-layered metal interconnects. We identify the main effect of DF, BP, and TS on both RR and film uniformity, as expected, by the statistical modeling and analysis on the metrology data acquired from a series of $2^{5-1}$ fractional factorial design with two center points. This revealed the film uniformity of the polished PSG film contains two and three-way interactions. Therefore, one can easily infer that the process control based on better understanding of the process is the key to success in semiconductor manufacturing, typically when the wafer size reaches 300 mm and is continuously scheduled to expand up to 450 mm in or little after 2012.

Bootstrap Estimation for the Process Incapability Index $C_{pp}$

  • Han, Jeong-Hye;Cho, Joong-Jae;Lim, Chun-Sung
    • Proceedings of the Korean Society for Quality Management Conference
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    • 1998.11a
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    • pp.309-315
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    • 1998
  • Process Capability can be expressed with a process index which indicates the incapability of a process to meet its specifications. This index is regarded as a process capability index(PCI) or more precisely as a process incapability index(PII). It is obtained from a simple transformation of a PCI. Greenwich and Jahr-Schaffrath(1995) considered the PII $C_{pp}$ which could be obtained from the transformation to the PCI, $C_{pm}$, and they provided the asymptotic distribution for $C_{pp}$ which was useful unless the process characteristic was normally distributed. However, some statistical inferences based on the asymptotic distribution need a large sample size. There are some processes which process engineers could not help obtaining sufficiently a large sample size. Thus, we have derived its corresponding bootstrap asymptotic distribution since bootstrapping would be a helpful technique for the PII, $C_{pp}$ which was nonparametric or free from assumptions of the distribution of the characteristic X. Moreover, we have constructed six bootstrap confidence intervals used in reducing bias of estimations based on the bootstrap asymptotic distribution and simulated their performances for $C_{pp}$,

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Residual spatial autocorrelation in macroecological and biogeographical modeling: a review

  • Gaspard, Guetchine;Kim, Daehyun;Chun, Yongwan
    • Journal of Ecology and Environment
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    • v.43 no.2
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    • pp.191-201
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    • 2019
  • Macroecologists and biogeographers continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data related to, for example, species abundance or presence/absence, climate, geomorphology, and soils. Researchers have acknowledged in their statistical analyses the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a degree of dependence between pairs of nearby observations. It has been agreed that residual spatial autocorrelation (rSAC) can have a substantial impact on modeling processes and inferences. However, more attention should be paid to the sources of rSAC and the degree to which rSAC becomes problematic. Here, we review previous studies to identify diverse factors that potentially induce the presence of rSAC in macroecological and biogeographical models. Furthermore, an emphasis is put on the quantification of rSAC by seeking to unveil the magnitude to which the presence of SAC in model residuals becomes detrimental to the modeling process. It turned out that five categories of factors can drive the presence of SAC in model residuals: ecological data and processes, scale and distance, missing variables, sampling design, and assumptions and methodological approaches. Additionally, we noted that more explicit and elaborated discussion of rSAC should be presented in species distribution modeling. Future investigations involving the quantification of rSAC are recommended in order to understand when rSAC can have an adverse effect on the modeling process.

Parallel Bayesian Network Learning For Inferring Gene Regulatory Networks

  • Kim, Young-Hoon;Lee, Do-Heon
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2005.09a
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    • pp.202-205
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    • 2005
  • Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

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