Statement of problem. There have been many studies about marginal discrepancy of single restorations made by various systems and materials. However most of the statistical inferences are not definite because of sample size, measurement number, measuring instruments, etc, and there have been few studies about the marginal fit of the Digident CAD/ CAM zirconia ceramic crowns. Purpose. The purpose of this study was to compare the marginal fit of the anterior single restorations made by using the Digident CAD / CAM zirconia ceramic crowns with metal-ceramic restorations and to obtain more accurate information by using a large enough sample size and by making sufficient measurements per specimen. Material and Methods. The crowns were made from one extracted maxillary central incisor pre-pared with a 1mm shoulder margin and $6^{\circ}$ taper walls by milling machine. The in vitro marginal discrepancies of the digident CAD / CAM zirconia ceramic crowns and control groups(metal ceramic crowns) were evaluated and compared. Twenty crowns per each system were fabricated. Measurements of a crown were recorded at 50 points that were randomly selected for marginal gap evaluation. Parametric statistical analysis was performed for the results. Conclusion. Within the limitations of this in vitro study, the following conclusions were drawn: 1. Mean gap dimensions and standard deviations at the marginal opening for maxillary incisal crowns were $88{\pm}10{\mu}m$ for the control (metal-ceramic crowns), $92{\pm}4{\mu}m$ for Digident CAD / CAM zirconia ceramic crowns. 2. Marginal gap between Digident CAD / CAM zirconia ceramic crowns and metal ceramic crowns did not show significant difference (P>.05). 3. The Digident CAD/ CAM zirconia ceramic crowns and metal ceramic crowns showed clinically acceptable marginal discrepancy.
It has long been of controversy what the meanings of probability is. And a century has past after the mathematical probability has been at the center of the school curriculum of it. Recently statistical meaning of probability becomes important for various reasons. However the simple modification of its definition is not enough. The computational reasoning of the probability and its practical application needs didactical changes and new instructional transformations along with the modification of it. Most of the current text books introduce probability as a limit of the relative frequencies, a statistical probability. But when the probability computation of the union of two events, or of the simultaneous events is faced on, they use mathematical probability for explanation and practices. Accordingly there is a gap for students in understanding those. Probability is an intuitive concept as far as it belongs to the domain of the experiential frequency. And frequency distribution must be the instructional bases for the (statistical) probability novices. This is what we mean by the probability in accordance with the distribution concepts. First of all, in order to explain the probability of the complementary event we should explain the empirical relative frequency of it first. These are the case for the union of two events and for the simultaneous events. Moreover we need to provide a logic of probabilistic guesses, inferences and decision, which we introduce with the name “the likelihood principle”, the most famous statistical principle. We emphasized this be done through the problems of practical decision making.
Khan, Hafiz Mohammad Rafiqullah;Saxena, Anshul;Gabbidon, Kemesha;Stewart, Tiffanie Shauna-Jeanne;Bhatt, Chintan
Asian Pacific Journal of Cancer Prevention
/
v.15
no.9
/
pp.4049-4054
/
2014
Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.
There have been many studies about marginal discrepancy of single restorations made by various systems and materials. But many of statistical inferences are not definite because of sample size, measurement number, measuring instruments. etc. The purpose of this study was to compare the marginal adaptations of the anterior single restorations made by different systems and to consider more desirable statistical methods in analysing the marginal fit. The in vitro marginal discrepancies of three different all-ceramic crown systems (Celay In-Ceram. Conventional In-Ceram. IPS Empress 2 layering technique) and one control group (PFM) were evaluated and compared. The crowns were made from one extracted maxillary central incisor prepared with a 1mm shoulder margin and $6^{\circ}$ taper walls by milling machine. 10 crowns per each system were fabricated. Measurements or a crown were recorded at 50 points that were randomly selected for marginal gap evaluation. Non-parametric statistical analysis was performed for the results. Within the limits of this study, the following conclusions were drawn: 1 Mean gap dimensions and standard deviations at the marginal opening for the maxillary incisor crowns were $98.2{\pm}40.6{\mu}m$ for PFM, $83.5{\pm}18.7{\mu}m$ for Celay In-Ceram, $104.9{\pm}44.1{\mu}m$ for conventional In-Ceram, and $45.5{\pm}11.5{\mu}m$ for IPS Empress 2 layering technique. The IPS Empress 2 system showed the smallest marginal gap (P<0.05). The marginal openings of the other three groups were not significantly different (P<0.05). 2 The marginal discrepancies found in this study were all within clinically acceptable standards ($100\sim150{\mu}m$). 3. When the variable is so controlled that the system may be the only one, mean value is interpreted to be the marginal discrepancy of a restoration which is made by each system and standard deviation is to be technique-sensitivity of each one. 4. From the standard deviations. the copy-milling technique (Celay/In-Ceram) was not considered to be technique-sensitive in comparison with other methods. 5. Parametric analysis is more reliable than non-parametric one in interpretation of the mean and standard deviation. The sample size of each group has to be more than 30 to use parametric statistics. The level of clinically acceptable marginal fit has not been established. Further studies are needed.
It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.
Journal of Korean Society for Geospatial Information Science
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v.7
no.1
s.13
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pp.29-40
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1999
In vector GIS, natural linear entities (called linear entitles) are usually represented by a set of line segments. As an alternative of the line segments, curve segments can be used to represent the linear entities. The curve segments, as one-dimensional spatial objects, we generated by spline interpolation technique such as Bezier technique. In an effort to improve its accuracy in resembling the linear entities, the Bezier technique was modified generating a new technique (called New technique) (Kiyun, 1998). In this paper, validity of the New technique was tested. Test focused on answering two questions: (1) whether or not the curve segments from the New technique replace line segments so as to enhance the accuracy of representations of linear entities, and (2) whether or not the curve segments from the New technique represent the linear entities more accurately than curve segments from the Bezier technique. Answering these two questions entailed two hypothesis tests. For test data, a series of hydrologic lines on 7.5-minute USGS map series were selected. Test were done using t-test method and statistical inferences were made from the results. Test results indicated that curve segments from both the Bezier and New techniques represent the linear entities more accurately than the line segments do. In addition, curve segments from the New technique represent the linear entities more accurately than the line segments from the Bezier technique do at probability level 69% or higher.
Due to the increasing concerns with the North Korean defectors, the researches have been increased.. However when compared with other areas, the researches about the North Korean defectors are rare. The sample size of this study is 171, which makes it possible to do statistical inferences. This study examines the adjustment stress and the effect of social support on the adaptation. As a demographic factors, gender, age and education are controlled. The context factors-the period of the stay in the third place, the period of residence and the existence of the education in Korea- are used as control variables. The research results show that the jobless is an important stressor. The structural social support affects the adjustment of stress as a whole and not individually. Among functional social support, the emotional social support is an important factor of adaptation, but the effect of structural social support is the opposite. Therefore, the adequacy of social support must be considered.
Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.
Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients' data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.
Journal of the Korean Society of Clothing and Textiles
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v.12
no.2
s.27
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pp.249-257
/
1988
The purposes of the study were to define the clothing evaluative criteria concept as an internal evaluative criteria and buying evaluative criteria based on the Engel-Kollot-Blackwell's consumer behavior model, and to explore the differences between internal evaluative criteria and buying evaluative criteria imprically. The study also aimed to find out the differences between internal evaluative criteria and buying evaluative criteria according to clothing items(underwears, pajama, jeans, blouse, two-pieces, coat) The questionnaires were administered to college female students living in Seoul. Means and Standard Deviations were calculated, and Discriminant Analysis were used for statistical inferences. The results were as follows: In underwears, internal evaluative criteria were comfort and fiber, while toying evaluative criteria was color. In pajama, internal evaluative criteria was comfort and fiber, while buying evaluative criteria was good buy. In jean, internal evaluative criteria was fit and suitability to individual, while buying evaluative criteria was suitability to individual and price. In blouse, internal evaluative criteria was beauty and harmony with other clothing. In two-pieces, internal evaluative criteria was color and beauty, while buying evaluative criteria was suitability to individual. In coat, internal evaluative criteria was textile and color. In conclusion, as shown in the results above, the evident differences between internal evaluative criteria and buying evaluative criteria and buying evaluative criteria were testified, therefore evaluative criteria should he defined as internal evaluative criteria and buying evaluative criteria, and study of evaluative criteria should be explored according to clothing items.
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