• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.773-786
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• 2016

Multiple quantile regression that simultaneously estimate several conditional quantiles of response given covariates can provide a comprehensive information about the relationship between the response and covariates. Some quantile estimates can cross if conditional quantiles are separately estimated; however, this violates the definition of the quantile. To tackle this issue, multiple quantile regression with non-crossing constraints have been developed. In this paper, we carry out a comparison study on several popular methods for non-crossing multiple linear quantile regression to provide practical guidance on its application.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.915-922
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• 2013

Quantile regression can estimate multiple conditional quantile functions of the response, and as a result, it provide comprehensive information of the relationship between the response and the predictors. However, when estimating several conditional quantile functions separately, two or more estimated quantile functions may cross or overlap and consequently violate the basic properties of quantiles. In this paper, we propose a new stepwise method to estimate multiple non-crossing quantile functions using constraints on the kernel coefficients. A simulation study are presented to demonstrate satisfactory performance of the proposed method.

• Asia-Pacific Journal of Business
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• v.12 no.4
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• pp.63-86
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• 2021

Purpose - The purpose of this study is to explore the impact of the application of accounting conservatism on the investment efficiency of listed companies in China under the background of the current rising environmental uncertainty. Design/methodology/approach - This study collected 14,934 observations of A-share listed companies in Shanghai and Shenzhen from 2013 to 2020, and analyzed the data by means of moderating effect test and multiple regression analysis. Findings - The results show that environmental uncertainty deteriorates the company's investment efficiency. The higher the level of environmental uncertainty, the more prone to over-investment and under-investment. Accounting conservatism plays moderating role between environmental uncertainty and investment efficiency. Among them, the moderating effect of conditional conservatism is to alleviate under-investment of the company under high financing constraints and the over-investment, while it intensifies the under-investment under low financing constraints. The moderating effect of unconditional conservatism is to alleviate the under-investment. Research implications or Originality - This study finds out the internal mechanism of accounting conservatism affecting investment efficiency, which not only helps to understand about the value of accounting conservatism standards, but also helps to improve the investment efficiency of listed companies.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.1-8
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• 2000

A spectrum minimizing the frequency-domain Kullback-Leibler information number has been proposed and used to modify a spectrum estimate. Some numerical examples have illustrated the KL spectrum estimate is superior to the initial estimate, i.e., the autocovariances obtained by the inverse Fourier transformation of the KL spectrum estimate are closer to the sample autocovariances of the given observations than those of the initial spectrum estimate. Also, it has been shown that a Gaussian autoregressive process associated with the KL spectrum is the closest in the timedomain Kullback-Leibler sense to a Gaussian white noise process subject to given autocovariance constraints. In this paper a corresponding conditional probability theorem is presented, which gives another rationale to the KL spectrum.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.155-159
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• 2013

In this paper, a nonlinear matrix inequality problem and a nonlinear optimization problem are proposed for obtaining a structured static output feedback controller. The proposed nonlinear optimization problem has LMI (Linear Matrix Inequality) constraints and a nonlinear objective function. Using the conditional gradient method, the nonlinear optimization problem can be solved. A numerical example shows the effectiveness of the proposed approach.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1447-1453
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• 2020

In this paper, we propose a method for no-reference quality assessment of sports videos using 3D shearlet transform and deep residual neural networks. In the proposed method, 3D shearlet transform-based spatiotemporal features are extracted from the overlapped video blocks and applied to logistic regression concatenated with a deep residual neural network based on a conditional video block-wise constraint to learn the spatiotemporal correlation and predict the quality score. Our evaluation reveals that the proposed method predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1646-1655
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• 1999

This thesis presents a new VHDL intermediate format CDDG(Control Dominated Data Graph) and a minimal constrained scheduling algorithm for an optimal control dominated ASIC design. CDDG is a control flow graph which represents conditional branches and loops efficiently. Also it represents data dependency and such constraints as hardware resource and timing. In the proposed scheduling algorithm, the constraints using the inclusion and overlap relation among subgraphs. The effectiveness of the proposed algorithm has been proven by the experiment with the benchmark examples.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.233-236
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• 2015

In this paper, a nonlinear optimization problem is proposed to obtain a structured static output feedback controller for discrete time linear systems. The proposed optimization problem has LMI (Linear Matrix Inequality) constraints and a non-convex objective function. Using the conditional gradient method, we can obtain suboptimal solutions of the proposed optimization problem. Numerical examples show the effectives of the proposed approach.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.