• Title/Summary/Keyword: Estimate method

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WEIGHTED NORM ESTIMATE FOR THE GENERAL HAAR SHIFT OPERATORS VIA ITERATING BELLMAN FUNCTION METHOD

  • CHUNG, DAEWON
    • East Asian mathematical journal
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    • v.31 no.5
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    • pp.635-652
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    • 2015
  • It is shown that for a general Haar shift operator, and a weight in the $A_2$ weight class, we establish the weighted norm estimate which linearly depends on $A_2$-characteristic $[w]_{A_2}$. Although the result is now well known, we introduce the new method, which is called the iterated Bellman function method, to provide the estimate.

Applying a New Approach to Estimate the Net Capital Stock of Transport Infrastructure by Region in South Korea

  • LEE, JONGYEARN
    • KDI Journal of Economic Policy
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    • v.40 no.2
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    • pp.23-52
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    • 2018
  • Given the limited availability of data in South Korea, this study proposes a method by which to estimate regional capital stock by modifying the benchmark year method (BYM) and applies it to estimate regional net capital stock by sector in transport infrastructure. First, it estimates time-varying sectoral depreciation rates using the sectoral net capital stock and the investment amount for each period. Second, it estimates the net capital stock of each period using the net capital stock in the base year and the investment in each period. Third, in order to ensure that the sum of net capital stocks by region is equal to the nationwide estimate, the national estimates are allocated to each region according to the proportion of the values derived from the previous stage. The proposed method can alleviate well-known problems associated with conventional BYMs, specifically the upward bias and arbitrary choice of the depreciation rate.

A Study on the ALS Method of System Identification (시스템동정의 ALS법에 관한 연구)

  • Lee, D.C.
    • Journal of Power System Engineering
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    • v.7 no.1
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    • pp.74-81
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    • 2003
  • A system identification is to estimate the mathematical model on the base of input output data and to measure the output in the presence of adequate input for the controlled system. In the traditional system control field, most identification problems have been thought as estimating the unknown modeling parameters on the assumption that the model structures are fixed. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input output case with the observed noise. We suggest the adjusted least squares method as a consistent estimation method in the system identification in the case where there is observed noise only in the output. In this paper the adjusted least squares method has been developed from the least squares method and the efficiency of the estimating results was confirmed by the generating data with the computer simulations.

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Comparison of Estimation Method of Pollutant Unit Loads from Bridge Area (교량지역의 다양한 비점오염물질 원단위 산정방법 비교)

  • Kim, Taewon;Gil, Kyungik
    • Journal of Korean Society on Water Environment
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    • v.27 no.5
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    • pp.597-604
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    • 2011
  • This research analyzed the runoff patterns and estimated unit loads of selected pollutatnts using monitored data conducted for three years in a bridge area. Three estimating methods; the arithmetic average method, the regression method and the rainfall class method were used to estimate the unit load. Results of three estimating methods were compared with the unit pollutant loads from landuses in Korea and the unit pollutant loads from urban watersheds in Milwaukee, USA. Unit load using the arithmetic mean method were found to be overestimated. In terms of TSS, unit loads of two estimate were half lower than that of USA. Estimated TN and TP unit loads of three estimate were lower than that of Ministry of Environment in Korea.

A Comparative Study of a Robust Estimate Method for Abnormal Traffic Detection (이상 트래픽 탐지를 위한 로버스트 추정 방법 비교 연구)

  • Jung, Jae-Yoon;Kim, Sahm
    • Communications for Statistical Applications and Methods
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    • v.18 no.4
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    • pp.517-525
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    • 2011
  • This paper shows the performance evaluation of a robust estimator based on the GARCH model. We first introduce the method of a robust estimate in the GARCH model and the method of an outlier detection in the GARCH model. The results of the real internet traffic data show the out-performance of the robust estimator over the outlier detection method in the GARCH model. In addition, the method of the robust estimate is less complex than the method of the outlier detection method in the GARCH model.

IRI estimation using analysis of dynamic tire pressure and axle acceleration

  • Zhao, Yubo;McDaniel, J. Gregory;Wang, Ming L.
    • Smart Structures and Systems
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    • v.19 no.2
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    • pp.151-161
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    • 2017
  • A new method is developed to estimate road profile in order to estimate IRI based on the ASTM standard. This method utilizes an accelerometer and a Dynamic Tire Pressure Sensor (DTPS) to estimate road roughness. The accelerometer measures the vertical axle acceleration. The DTPS, which is mounted on the tire's valve stem, measures dynamic pressure inside the tire while driving. Calibrated transfer functions are used to estimate road profile using the signals from the two sensors. A field test was conducted on roads with different quality conditions in the city of Brockton, MA. The IRI values estimated with this new method match the actual road conditions measured with Pavement Condition Index (PCI) based on the ASTM standard, images taken from an onboard camera and passengers' perceptions. IRI has negative correlation with PCI in general since they have overlapping features. Compared to the current method of IRI measurement, the advantage of this method is that a) the cost is reduced; b) more space is saved; c) more time is saved; and d) mounting the two sensors are universally compatible to most cars and vans. Therefore, this method has the potential to provide continuous and global monitoring the health of roadways.

Pollutant Loading Estimates from Watershed by Rating Curve Method and SWMM

  • Jeon, Ji-Hong;Yoon, Chun-Gyeong
    • Korean Journal of Environmental Agriculture
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    • v.19 no.5
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    • pp.419-425
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    • 2000
  • Rating curve method and SWMM (Storm Water Management Model) were applied to estimate pollutant loading from Hwa-Ong watershed in Kyunggi-Do. Rating curves were derived from sampling sites and applied to the whole watershed. SWMM version 4.4 was calibrated by field data of sampling sites and applied to the whole watershed. The pollutant loading estimated by rating curve was slightly higher than the one by SWMM, but the difference was not significant considering diffuse pollution characteristics of wide variation. Land use effect of the subcatchments could not be incorporated logically in rating curve method and difficulty in extrapolation was experienced, therefore, the estimate by rating curve method was thought to be less confident. SWMM was satisfactory in estimation of pollution loading, and its great flexibility worked well to describe complex nonurban land uses. Neither of them could exactly describe complex natural phenomena, but SWMM was preferred in this study due to its flexibility and logical hydrologic processes including land use effects. Use of reasonable watershed model rather than rating curve method for watershed pollutant loading estimate can be more practical and is recommended.

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Estimation of Mixture Numbers of GMM for Speaker Identification (화자 식별을 위한 GMM의 혼합 성분의 개수 추정)

  • Lee, Youn-Jeong;Lee, Ki-Yong
    • Speech Sciences
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    • v.11 no.2
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    • pp.237-245
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    • 2004
  • In general, Gaussian mixture model(GMM) is used to estimate the speaker model for speaker identification. The parameter estimates of the GMM are obtained by using the expectation-maximization (EM) algorithm for the maximum likelihood(ML) estimation. However, if the number of mixtures isn't defined well in the GMM, those parameters are obtained inappropriately. The problem to find the number of components is significant to estimate the optimal parameter in mixture model. In this paper, to estimate the optimal number of mixtures, we propose the method that starts from the sufficient mixtures, after, the number is reduced by investigating the mutual information between mixtures for GMM. In result, we can estimate the optimal number of mixtures. The effectiveness of the proposed method is shown by the experiment using artificial data. Also, we performed the speaker identification applying the proposed method comparing with other approaches.

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EVALUATION OF TECHNIQUES FOR ESTIMATING MILK PRODUCTION BY SOWS 2. ESTIMATING THE MILK CONSUMPTION OF PIGLETS BY THE DEUTERIUM OXIDE DILUTION AND WEIGH-SUCKLE-WEIGH METHODS

  • Prawirodigdo, S.;King, R.H.;Dunkin, A.C.;Dove, H.
    • Asian-Australasian Journal of Animal Sciences
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    • v.3 no.2
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    • pp.143-148
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    • 1990
  • An experiment was conducted to compare the traditional weigh-suckle-weigh method and the $D_2O$ dilution technique to estimate milk consumption of suckling piglets. Milk consumption of 50 individual piglets was estimated on four consecutive days by the $D_2O$ dilution method and for approximately 8 hours on both the second and fourth day by the traditional WSW method. The average milk intake of piglets estimated by the $D_2O$ dilution method was 45.0 g/hr and there were no significant differences between the four measurement period. The traditional weigh-suckle-weigh method provided a significantly lower estimate of milk consumption (36.8 g/hr). However correction for weight losses associated with milk suckling and weighing would increase the weigh-suckle-weigh estimate to a level similar to that determined by the $D_2O$ dilution method.

Exact Variance of Location Estimator in One-Way Random Effect Models with Two Distint Group Sizes

  • Lee, Young-Jo;Chung, Han-Yeong
    • Journal of the Korean Statistical Society
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    • v.18 no.2
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    • pp.118-124
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    • 1989
  • In the one-way random effect model, we often estimate the variance components by the ANOVA method and then estimate the population mean. Whe there are only two distint group sizes, the conventional mean estimator is represented as a weighted average of two normal means with weights being the function of variance component estimators. In this paper, we will study a method which can compute the exact variance of the mean estimator when we set the negative variance component estimate to zero.

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