• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.176-181
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• 1990

Sung [1990] presented an analogue of the classical Cramer-Rao inequality for median-unbiased estimators with continuous multivariate densities depending upon a vector parameter. In the process, diffusivity, a new dispersion measure relevant to median-unbiased estimators, was defined to be a function of median-unbiased estimator's density height. In this paper we shall elaborate these ideas by defining a second kind of diffusivity and discuss the role of model-unbiasedness in median-unbiased estimation in connection with this seconde kind of diffusivity. In addition, median-unbiased estimation will be compared to mean-unbiased estimation.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.977-994
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• 1999

Five equating methos for elective subjects of College Scholastic Ability Test were analyzed under a common-items nonequivalent groups design using a real data set of 110 thousand examinees. Five methods were (a)two-stage linear equating (b) two-stage equi-percentile equating (c) Tucker equating (d) Frequency estimation equating and (e)Braun-Holland equating. The results indicated that Frequency estimation equating fits well and two-stage linear equating produces most different equating results from the Frequency estimation equating.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.337-344
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• 1998

We analyze statistical reliability about local area results of '96 Survey of Family Consumption Behavior by the National Statistical Office. Also, we suggest reliable estimation procedures for reducing statistical errors in local ares estimation problem.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.161-173
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• 1996

When we deal with a Hilbert space-valued Stochastic Differential Equation (SDE) (or Stochastic Partial Differential Equation (SPDE)), depending on some unknown parameters, the solution usually has a Fourier series expansion. In this situation we consider the maximum likelihood method for the statistical estimation problem and derive the asymptotic properties (consistency and normality) of the Maximum Likelihood Estimator (MLE).

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.125-134
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• 2003

Standard estimation methods for linear mixed models are sensitive to influential observations. However, tools and concepts for linear mixed model diagnostics are rudimentary until now and research is heavily demanded in linear mixed models. In this paper, we consider two diagnostics to evaluate the effects of individual observations in the estimation of fixed effects for linear mixed models. Those are Cook's distance and COVRATIO. Results of our limited simulation study suggest that the Cook's distance is not good statistical quantity in linear mixed models. Also calibration point for COVRATIO seems to be quite conservative.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.889-898
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• 2002

In decision theoretic estimation, the loss function usually emphasizes precision of estimation. However, one may have interest in goodness of fit of the overall model as well as precision of estimation. From this viewpoint, Zellner(1994) proposed the balanced loss function which takes account of both "goodness of fit" and "precision of estimation". This paper considers estimation of the parameter of a Bernoulli distribution using Zellner's(1994) balanced loss function. It is shown that the sample mean $\overline{X}$, is admissible. More general results, concerning the admissibility of estimators of the form $a\overline{X}+b$ are also presented. Finally, minimax estimators and some numerical results are given at the end of paper,at the end of paper.

• Annals of Clinical Neurophysiology
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• v.5 no.1
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• pp.27-33
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• 2003

Background: Motor unit number estimation (MUNE) can directly assess motor neuron populations in muscle and quantify the degree of physiologic and/or pathologic motor neuron degeneration. A high degree of reproducibility and reliability is required from a good quantitative tool. MUNE, in various ways, is being increasingly applied clinically and statistical MUNE has several advantages over alternative techniques. Nevertheless, the optimal method of applying statistical MUNE to improve reproducibility has not been established. Methods: We performed statistical MUNE by selecting the most compensated compound muscle action potential (CMAP) area as a test area and modified the results obtained by weighted mean surface-recorded motor unit potential (SMUP). Results: MUNE measures in amyotrophic lateral sclerosis (ALS) patients showed better reproducibility with sizeweighted modification. Conclusions: We suggest size-weighted MUNE testing of "neurogenically compensated"CMAP areas present an optimal method for statistical MUNE in ALS patients.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.519-531
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• 2017

We consider goodness-of-fit test statistics for Weibull distributions when data are randomly censored and the parameters are unknown. Koziol and Green (Biometrika, 63, 465-474, 1976) proposed the $Cram\acute{e}r$-von Mises statistic's randomly censored version for a simple hypothesis based on the Kaplan-Meier product limit of the distribution function. We apply their idea to the other statistics based on the empirical distribution function such as the Kolmogorov-Smirnov and Liao and Shimokawa (Journal of Statistical Computation and Simulation, 64, 23-48, 1999) statistics. The latter is a hybrid of the Kolmogorov-Smirnov, $Cram\acute{e}r$-von Mises, and Anderson-Darling statistics. These statistics as well as the Koziol-Green statistic are considered as test statistics for randomly censored Weibull distributions with estimated parameters. The null distributions depend on the estimation method since the test statistics are not distribution free when the parameters are estimated. Maximum likelihood estimation and the graphical plotting method with the least squares are considered for parameter estimation. A simulation study enables the Liao-Shimokawa statistic to show a relatively high power in many alternatives; however, the null distribution heavily depends on the parameter estimation. Meanwhile, the Koziol-Green statistic provides moderate power and the null distribution does not significantly change upon the parameter estimation.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.