• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.279-293
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• 2021

In this paper, we introduce how to construct sufficient conditions for the oracle property in penalized linear regression model. We give formal definitions of the oracle estimator, penalized estimator, oracle penalized estimator, and the oracle property of the oracle estimator. Based on the definitions, we present a unified way of constructing optimality conditions for the oracle property and sufficient conditions for the optimality conditions that covers most of the existing penalties. In addition, we present an illustrative example and results from the numerical study.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.393-408
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• 2024

The sparse linear discriminant analysis can be incorporated into the penalized linear regression framework, but most studies have been limited to specific convex penalties, including the least absolute selection and shrinkage operator and its variants. Within this framework, concave penalties can serve as natural counterparts of the convex penalties. Implementing the concave penalized direction vector of discrimination appears to be straightforward, but developing its theoretical properties remains challenging. In this paper, we explore a class of concave penalties that covers the smoothly clipped absolute deviation and minimax concave penalties as examples. We prove that employing concave penalties guarantees an oracle property uniformly within this penalty class, even for high-dimensional samples. Here, the oracle property implies that an ideal direction vector of discrimination can be exactly recovered through concave penalized least squares estimation. Numerical studies confirm that the theoretical results hold with finite samples.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.445-458
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• 2020

The least absolute shrinkage and selection operator (LASSO) is a popular method for a high-dimensional regression model. LASSO has high prediction accuracy; however, it also selects many irrelevant variables. In this paper, we consider the moderately clipped LASSO (MCL) for the high-dimensional generalized linear model which is a hybrid method of the LASSO and minimax concave penalty (MCP). The MCL preserves advantages of the LASSO and MCP since it shows high prediction accuracy and successfully selects relevant variables. We prove that the MCL achieves the oracle property under some regularity conditions, even when the number of parameters is larger than the sample size. An efficient algorithm is also provided. Various numerical studies confirm that the MCL can be a better alternative to other competitors.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.453-470
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• 2018

We study how to distinguish the parameters of the sparse autoregressive (AR) process from zero using a non-convex penalized estimation. A class of non-convex penalties are considered that include the smoothly clipped absolute deviation and minimax concave penalties as special examples. We prove that the penalized estimators achieve some standard theoretical properties such as weak and strong oracle properties which have been proved in sparse linear regression framework. The results hold when the maximal order of the AR process increases to infinity and the minimal size of true non-zero parameters decreases toward zero as the sample size increases. Further, we construct a practical method to select tuning parameters using generalized information criterion, of which the minimizer asymptotically recovers the best theoretical non-penalized estimator of the sparse AR process. Simulation studies are given to confirm the theoretical results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1904-1919
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• 2015

Deniable authentication protocol allows a sender to deny his/her involvement after the protocol run and a receiver can identify the true source of a given message. Meanwhile, the receiver has no ability to convince any third party of the fact that the message was sent by the specific sender. However, most of the proposed protocols didn't achieve confidentiality of the transmitted message. But, in some special application scenarios such as e-mail system, electronic voting and Internet negotiations, not only the property of deniable authentication but also message confidentiality are needed. To settle this problem, in this paper, we present a non-interactive identity-based deniable authenticated encryption (IBDAE) scheme using pairings. We give the security model and formal proof of the presented IBDAE scheme in the random oracle model under bilinear Diffie-Hellman (BDH) assumption.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.453-480
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• 2010

Cryptographic hash functions reduce inputs of arbitrary or very large length to a short string of fixed length. All hash function designs start from a compression function with fixed length inputs. The compression function itself is designed from scratch, or derived from a block cipher or a permutation. The most common procedure to extend the domain of a compression function in order to obtain a hash function is a simple linear iteration; however, some variants use multiple iterations or a tree structure that allows for parallelism. This paper presents a survey of 17 extenders in the literature. It considers the natural question whether these preserve the security properties of the compression function, and more in particular collision resistance, second preimage resistance, preimage resistance and the pseudo-random oracle property.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2762-2777
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• 2017

A fuzzy identity based signature (FIBS) scheme allows a signer with identity ${\omega}$ to generate a signature which could be verified under identity ${\omega}^{\prime}$ if and only if ${\omega}$ and ${\omega}^{\prime}$ are within a certain distance of each other as judged by some metric. In this paper, we propose an efficient FIBS scheme from lattice assumption, which can resist quantum-computer attacks. Without using the Bonsai Tree technique, we utilize the lattice basis delegation technique to generate the private key, which has the advantage of keeping the lattice dimension invariant. We also prove that our proposed scheme is existentially unforgeable under an adaptive chosen message and identity attack in the random oracle model. Compared with existing scheme, our proposed scheme is much more efficient, especially in terms of communication overhead. Since our FIBS scheme possesses similar error-tolerance property, it can be well applied in post-quantum communication biometric authentication environments, where biometric identifiers such as fingerprints, voice, iris and gait are used in human identification.

• Management & Information Systems Review
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• v.26
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• pp.27-60
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• 2008

Researches on the introduction of ERP system kept on examining the critical successful factors (CSFs) that focus on factors to achieve effectively successful projects, and trying to measuring the actual effectiveness of the introduction of ERP system. However, most of the preceding researches on the effectiveness of the introduction of ERP system that was searching devoted effects has been ceased, and actually even researches on the economical results have just done the basic cognitive evaluation of result indicators by many questionnaires instead of objective measuring values, because of the difficulty of measuring the evaluation of the result. Moreover, researches on positive effects of the introduction of ERP on enterprise results and researches that failed to give advantageous effects showed different results each other. And a part of researches reported that only a part of result indicators were partially affected. In this research, we investigated Korean large enterprises or middle-sized enterprises in manufacture industry that introduces SAP R/3 and Oracle package to compare their quantitative financial results after the introduction of ERP system, in order to measure the effects of the ERP system. First, we evaluated the difference of the quantitative financial results before and after the introduction of the ERP system. Second, we evaluated the opportunities shown by the effects after the introduction of the ERP system. Third, we removed the sample of the exchange crisis (IMF) and executed the additional analysis to reflect the average increasing and decreasing rate in the industry, so that pure evaluation can be achieved. Inherent limits of precedent researches are removed and practical effects of the pure introduction of the ERP system are evaluated, so the research of this research is significant. The result of this research is as follows. Because of the introduction of ERP, the rate of turnover of inventory property has increased and sales of preparation inventory property have decreased so that more effective inventory property management has been achieved. Moreover, preparation sales of labor costs and preparation sales of the number of employees have decreased to show the effect of the reduction of labor costs. However, it could no be concluded that we could increase the profit due to the introduction of ERP system. Due to the introduction of ERP, although we concluded that the return on assets (ROA) and the additional value of one-person employee statistically showed obvious differences and increased, the return on equity failed to show obvious differences after the process of introduction of ERP.