• Title/Summary/Keyword: Interest Estimation

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Effects of the Misspecification of Cointegrating Ranks in Seasonal Models

  • Seong, Byeong-Chan;Cho, Sin-Sup;Ahn, Sung-K.;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.21 no.5
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    • pp.783-789
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    • 2008
  • We investigate the effects of the misspecification of cointegrating(CI) ranks at other frequencies on the inference of seasonal models at the frequency of interest; our study includes tests for CI ranks and estimation of CI vectors. Earlier studies focused mostly on a single frequency corresponding to one seasonal root at a time, ignoring possible cointegration at the remaining frequencies. We investigate the effects of the mis-specification, especially in finite samples, by adopting Gaussian reduced rank(GRR) estimation by Ahn and Reinsel (1994) that considers cointegration at all frequencies of seasonal unit roots simultaneously. It is observed that the identification of the seasonal CI rank at the frequency of interest is sensitive to the mis-prespecification of the CI ranks at other frequencies, mainly when the CI ranks at the remaining frequencies are underspecified.

A Study on Signal Parameters Estimation via Nonlinear Minimization

  • Jeong, Jung-Sik
    • Journal of Navigation and Port Research
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    • v.28 no.4
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    • pp.305-309
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    • 2004
  • The problem for parameters estimation of the received signals impinging on array sensors has long been of great research Interest in a great variety of applications, such as radar, sonar, and land mobile communications systems. Conventional subspace-based algorithms, such as MUSIC and ESPRIT, require an extensive computation of inverse matrix and eigen-decomposition In this paper, we propose a new parameters estimation algorithm via nonlinear minimization, which is simplified computationally and estimates signal parameters simultaneously.

Effects of Bank Macroeconomic Indicators on the Stability of the Financial System in Indonesia

  • VIPHINDRARTIN, Sebastiana;ARDHANARI, Margaretha;WILANTARI, Regina Niken;SOMAJI, Rafael Purtomo;ARIANTI, Selvi
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.1
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    • pp.647-654
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    • 2021
  • This study examines the non-performing loans of rural banks and macroeconomic factors in Indonesia, including inflation, exchange rates, and interest rates. Theoretically, the existence of erratic macroeconomic conditions can affect the level of non-performing credit risk in rural credit banks in Indonesia. The effect of macroeconomic conditions on non-performing loans has a different response for each economic sector. The main objective of this study is to determine the effect of macroeconomic factors (inflation, exchange rates, and interest rates) and bank-specific factors (credit) on the Non-Performing Loans (NPL) of Rural Banks in Indonesia for the period from January 2015 to December 2018. This study uses a Vector Error Correction Model (VECM) estimation to determine the effect of independent variables consisting of macroeconomic factors and bank-specific factors. Based on the estimation results of the Vector Error Correction Model, three variables that have a positive and significant effect on long-term non-performing loans are credit, inflation, and interest rates. Meanwhile, in the short term, there are only two variables that have a positive and significant effect on non-performing loans, namely, credit and interest rates. Inflation and exchange rate variables have a negative and insignificant effect on bad credit in the short term.

Gaze Mirroring-based Intelligent Information System for Making User's Latent Interest (사용자의 잠재적 흥미를 인식하기 위한 주시 모방 모델 기반의 지능형 정보 시스템)

  • Park, Hye-Sun;Hirayama, Takatsugu;Matsuyama, Takashi
    • Journal of Intelligence and Information Systems
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    • v.16 no.3
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    • pp.37-54
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    • 2010
  • The information system that preserves and presents information collections, records, processes, retrievals, is applied in various fields recently and is supporting man's many activities. Conventional information systems are based on the reactive interaction model. Such reactive systems respond to only specific instructions, i.e. the defined commands, from the user. To go beyond the reactive interaction, it is necessary that the interactive dynamic interaction based information system which understands human's action and intention autonomously and then provides sensible information adapted to the user. Therefore, we propose a Gaze Mirroring-based intelligent information system for making user's latent interest using the internal state estimation methods based on the interactive dynamic interaction. Then, the proposed Gaze Mirroring method is that an anthropomorphic agent(avatar) actively established the joint attention with the user by imitating user's eye-gaze behavior. We verify that the Gaze Mirroring can elicit the user's behavior reflecting the latent interestand contribute to improving the accuracy of interest estimation. We also have confidence that the Gaze Mirroring promotes the self-awareness of interest. Such a Gaze Mirroring-based intelligent information system also provides suitable information to user by making user's latent interest using the internal state estimation.

APPROACHES TO SAMPLE SIZE ESTIMATION IN THE DESIGN OF CLINICAL TRIALS-A REVIEW

  • Donner Allan
    • 대한예방의학회:학술대회논문집
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    • 1994.02b
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    • pp.297-312
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    • 1994
  • Over the last decade, considerable interest has focused on sample size estimation in the design of clinical trials. The resulting literature is scattered over many textbooks and journals. This paper presents these methods in a single review and comments on their application in practice.

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A New Motion Compensated Frame Interpolation Algorithm Using Adaptive Motion Estimation (적응적 움직임 추정 기법을 활용하는 새로운 움직임 보상 프레임 보간 알고리즘)

  • Hwang, Inseo;Jung, Ho Sun;Sunwoo, Myung Hoon
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.6
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    • pp.62-69
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    • 2015
  • In this paper, a new frame rate up conversion (FRUC) algorithm using adaptive motion estimation (AME-FRUC) is proposed. The proposed algorithm performs extended bilateral motion estimation (EBME) conducts motion estimation (ME) processes on the static region, and extract region of interest with the motion vector (MV). In the region of interest block, the proposed AME-FRUC uses the texture block partitioning scheme and the unilateral motion estimation for improving ME accuracy. Finally, motion compensated frame interpolation (MCFI) are adopted to interpolate the intermediate frame in which MCFI is employed adaptively based on ME scheme. Experimental results show that the proposed algorithm improves the PSNR up to 3dB, the SSIM up to 0.07 and 68% lower SAD calculations compared to the EBME and the conventional FRUC algorithms.

Object detection within the region of interest based on gaze estimation (응시점 추정 기반 관심 영역 내 객체 탐지)

  • Seok-Ho Han;Hoon-Seok Jang
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.16 no.3
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    • pp.117-122
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    • 2023
  • Gaze estimation, which automatically recognizes where a user is currently staring, and object detection based on estimated gaze point, can be a more accurate and efficient way to understand human visual behavior. in this paper, we propose a method to detect the objects within the region of interest around the gaze point. Specifically, after estimating the 3D gaze point, a region of interest based on the estimated gaze point is created to ensure that object detection occurs only within the region of interest. In our experiments, we compared the performance of general object detection, and the proposed object detection based on region of interest, and found that the processing time per frame was 1.4ms and 1.1ms, respectively, indicating that the proposed method was faster in terms of processing speed.

Improving $L_1$ Information Bound in the Presence of a Nuisance Parameter for Median-unbiased Estimators

  • Sung, Nae-Kyung
    • Journal of the Korean Statistical Society
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    • v.22 no.1
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    • pp.1-12
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    • 1993
  • An approach to make the information bound sharper in median-unbiased estimation, based on an analogue of the Cramer-Rao inequality developed by Sung et al. (1990), is introduced for continuous densities with a nuisance parameter by considering information quantities contained both in the parametric function of interest and in the nuisance parameter in a linear fashion. This approach is comparable to that of improving the information bound in mean-unbiased estimation for the case of two unknown parameters. Computation of an optimal weight corresponding to the nuisance parameter is also considered.

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Estimation of the Parameter of a Bernoulli Distribution Using a Balanced Loss Function

  • Farsipour, N.Sanjari;Asgharzadeh, A.
    • 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.

Distributed Estimation Using Non-regular Quantized Data

  • Kim, Yoon Hak
    • Journal of information and communication convergence engineering
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    • v.15 no.1
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    • pp.7-13
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    • 2017
  • We consider a distributed estimation where many nodes remotely placed at known locations collect the measurements of the parameter of interest, quantize these measurements, and transmit the quantized data to a fusion node; this fusion node performs the parameter estimation. Noting that quantizers at nodes should operate in a non-regular framework where multiple codewords or quantization partitions can be mapped from a single measurement to improve the system performance, we propose a low-weight estimation algorithm that finds the most feasible combination of codewords. This combination is found by computing the weighted sum of the possible combinations whose weights are obtained by counting their occurrence in a learning process. Otherwise, tremendous complexity will be inevitable due to multiple codewords or partitions interpreted from non-regular quantized data. We conduct extensive experiments to demonstrate that the proposed algorithm provides a statistically significant performance gain with low complexity as compared to typical estimation techniques.