• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.321-334
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• 2016

In this paper, we propose power t distribution based on t distribution. We also study the properties of and inferences for power t model in order to solve the problem of real data showing both skewness and heavy tails. The comparison of skew t and power t distributions is based on density plots, skewness and kurtosis. Note that, at the given degree of freedom, the kurtosis's range of the power t model surpasses that of the skew t model at all times. We draw inferences for two parameters of the power t distribution and four parameters of the location-scale extension of power t distribution via maximum likelihood. The Fisher information matrix derived is nonsingular on the whole parametric space; in addition we obtain the profile log-likelihood functions on two parameters. The response plots for different sample sizes provide strong evidence for the estimators' existence and unicity. An application of the power t distribution suggests that the model can be very useful for real data.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.773-779
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• 2015

We study a statistical analysis about the fifth wave data of the Korea National Health and Nutrition Examination Survey based on linear regression models with measurement errors. The data is obtained from a national population-based complex survey. To demonstrate the availability of measurement error models, two results between the general linear regression model and measurement error model are compared based on the model selection criteria which are Akaike information criterion and Bayesian information criterion. For our study, we use the simulation extrapolation algorithm for measurement error model and the jackknife method for the estimation of standard errors.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.1303-1306
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• 2015

Studies of epidemiological and prognostic factors are very important for oncology practice. There is a rapidly increasing amount of research and resultant knowledge in the scientific literature. This means that health professionals have major challenges in accessing relevant information and they increasingly require best available evidence to make their clinical decisions. Meta-analyses of prognostic and other epidemiological factors are very practical statistical approaches to define clinically important parameters. However, they also feature many obstacles in terms of data collection, standardization of results from multiple centers, bias, and commentary for intepretation. In this paper, the obstacles of meta-analysis are briefly reviewed, and potential problems with this statistical method are discussed.

• Transactions on Electrical and Electronic Materials
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• v.6 no.3
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• pp.87-90
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• 2005

$SiO_2$ via-hole etching with a high aspect ratio is a key process in fabricating ULSI devices; however, accumulated charge during plasma etching can cause etching stop, micro-loading effects, and charge build-up damage. To alleviate this concern, charge-up damage sensor was fabricated for the ultimate goal of real-time monitoring of accumulated charge. As an effort to reach the ultimate goal, fabricated sensor was used for electrical potential measurements of via holes between two poly-Si electrodes and roughly characterized under various plasma conditions using statistical design of experiment (DOE). The successful identification of potential difference under various plasma conditions not only supports the evidence of potential charge-up damage, but also leads the direction of future study.

• International Journal of High-Rise Buildings
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• v.5 no.2
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• pp.137-144
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• 2016

This paper discusses the economics of skyscraper construction in Manhattan since 1990. First the paper reviews the economic theory of skyscraper height. Next it documents the frequency and heights of skyscraper construction in the last 25 years. Then the paper reviews the relative movements of office rents, condominium prices, and construction costs. Statistical results suggest that the resurgence of Manhattan's skyscraper construction is being driving by the rise in the average price of apartments, and is not being driven by rising office rents or falling construction costs. Statistical evidence shows that the height premium has not been rising over the last decade. Developers have been purchasing air rights (and bidding up the prices) in order to satisfy the demand for supertall buildings. In the next five to ten years, Manhattan is likely to see over thirty 200-meter or taller buildings, as compared to only four since 2010.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• Food Science and Biotechnology
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• v.15 no.4
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• pp.494-498
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• 2006

An increase in variance (overdispersion) can occur when a binomial statistical analysis is applied to sensory difference test data in which replicate sensory evaluations (tastings) and multiple evaluators (judges) are combined to increase the sample size. Such a practice can cause extensive Type I errors, leading to serious misinterpretations of the data, especially when traditional simple binomial analysis is applied. Alternatively, the use of beta binomial analysis will circumvent the problem of overdispersion. This brief review discusses the uses and computation methodology of beta binomial analysis and in practice evidence for the occurrence of overdispersion.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.675-688
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• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.27-40
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• 2022

In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model. The observation and the latent variable are the degraded image and the original image, respectively. The posterior distribution is built using the Markov random field and the Ising model. Estimating the posterior distribution is the same as reconstructing a degraded image. MCMC and variational Bayesian inference are two methods for estimating the posterior distribution. However, for the sake of computing efficiency, we adapt the variational technique. When the image is restored, the iterative method is used to solve the recursive problem. Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state. Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.2
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• pp.153-167
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• 1997

There have been many studies concerning the relationships between stock returns and volatilities. Their positive relationship is well known from the theoretical point of view, but not empirically shown. Franch, Schwert and Stambaugh [11] has empirically provided the indirect evidence of the positive relationship betwen expected stock return and expected volatility. However, their study lacks some statistical validity. This study reexamines the relationship using regression diagnostics and GARCH model from an international point of view. The empirical results fall to show the positive relationship between expected stock return and expected volaiility, which contradicts those of France, Schwert and Stambangh [1].