• Title/Summary/Keyword: Statistical efficiency

Search Result 1,210, Processing Time 0.022 seconds

Multistage Point and Confidence Interval Estimation of the Shape Parameter of Pareto Distribution

  • Hamdy, H.I.;Son, M.S.;Gharraph, M.K.;Rashad, A.M.
    • Communications for Statistical Applications and Methods
    • /
    • v.10 no.3
    • /
    • pp.1069-1086
    • /
    • 2003
  • This article presents the asymptotic theory of triple sampling procedure as pertain to estimating the shape parameter of Pareto distribution. Both point and confidence interval estimation are considered within the same inference unified framework. We show that this group sampling technique possesses the efficiency of Anscome (1953), Chow and Robbins (1965) purely sequential procedure as well as reduce the number of sampling operations by utilizing Stein (1945) two stages procedure. The analysis reveals that the technique performs excellent as far as the accuracy is concerned. The present problem differs from those considered by many authors, in multistage sampling, in that the final stage sample size and the parameter's estimate become highly correlated and therefore we adopted different approach.

Analysis of Sewage Plant Operation by Statistical Approach (통계방법에 의한 하수처리장 운전분석)

  • 이찬형;문경숙
    • Journal of Environmental Health Sciences
    • /
    • v.28 no.3
    • /
    • pp.34-38
    • /
    • 2002
  • Statistical analysis between sewage plant operating parameters and the effluent quality was performed. We extracted two factors from principal component analysis of operating parameters and effluent quality from each plant. The total variance of 84.7%, 79.2% was explained by the two factors at SB plant and SC plant, respectively. The factors were identified at SB plant in the following order 1) the oxidation of organic material by aeration basin microbe,2) biomass in aeration basin and at SC plant 1) the oxidation of organic material by aeration basin microbe, 2) thickening of acti-vated sludge. These results suggested that the control of microbial composition might be critical on the improvement of the effluent quality and plant operating efficiency because most of the factors were related with microbes.

Folded Ranked Set Sampling for Asymmetric Distributions

  • Bani-Mustafa, Ahmed;Al-Nasser, Amjad D.;Aslam, Muhammad
    • Communications for Statistical Applications and Methods
    • /
    • v.18 no.1
    • /
    • pp.147-153
    • /
    • 2011
  • In this paper a new sampling procedure for estimating the population mean is introduced. The performance of the new population mean estimator is discussed, along with its properties, and it is shown that the proposed method generates an unbiased estimator. The relative efficiency of the suggested estimator is computed, in regards to the simple random sample(SRS), and comparisons are made to the ranked set sampling(RSS) and extreme ranked set sampling(ERSS) estimators used for asymmetric distributions. The results indicate that the proposed estimator is more efficient than the estimators based on the ERSS. In addition, the folded ranked set sampling(FRSS) procedure has an advantage over the RSS and ERSS in that it reduces the number of unused sampling units.

Two-Stage Penalized Composite Quantile Regression with Grouped Variables

  • Bang, Sungwan;Jhun, Myoungshic
    • Communications for Statistical Applications and Methods
    • /
    • v.20 no.4
    • /
    • pp.259-270
    • /
    • 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.

Comparison of BP and SOM as a Classification of PD Source (부분방전원의 분류에 있어서 BP와 SOM의 비교)

  • 박성희;강성화;임기조
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
    • /
    • v.17 no.9
    • /
    • pp.1006-1012
    • /
    • 2004
  • In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. Two learning schemes are used to classification; BP(Back propagation algorithm), SOM(self organized map - kohonen network). As a PD source, using treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And a]so these distribution characteristics are applied to classify PD sources by two scheme of the neural networks. In conclusion, recognition efficiency of BP is superior to SOM.

On Quantifies Estimation Using Ranked Samples with Some Applications

  • Samawi, Hani-M.
    • Journal of the Korean Statistical Society
    • /
    • v.30 no.4
    • /
    • pp.667-678
    • /
    • 2001
  • The asymptotic behavior and distribution for quantiles estimators using ranked samples are introduced. Applications of quantiles estimation on finding the normal ranges (2.5% and 97.5% percentiles) and the median of some medical characteristics and on finding the Hodges-Lehmann estimate are discussed. The conclusion of this study is, whenever perfect ranking is possible, the relative efficiency of quantiles estimation using ranked samples relative to SRS is high. This may translates to large savings in cost and time. Also, this conclusion holds even if the ranking is not perfect. Computer simulation results are given and real data from lows 65+ study is used to illustrate the method.

  • PDF

A Hybrid Algorithm for Identifying Multiple Outlers in Linear Regression

  • Kim, Bu-yong;Kim, Hee-young
    • Communications for Statistical Applications and Methods
    • /
    • v.9 no.1
    • /
    • pp.291-304
    • /
    • 2002
  • This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

GENERALIZING THE REFINED PICKANDS ESTIMATOR OF THE EXTREME VALUE INDEX

  • Yun, Seok-Hoon
    • Journal of the Korean Statistical Society
    • /
    • v.33 no.3
    • /
    • pp.339-351
    • /
    • 2004
  • In this paper we generalize and improve the refined Pickands estimator of Drees (1995) for the extreme value index. The finite-sample performance of the refined Pickands estimator is not good particularly when the sample size n is small. For each fixed k = 1,2,..., a new estimator is defined by a convex combination of k different generalized Pickands estimators and its asymptotic normality is established. Optimal weights defining the estimator are also determined to minimize the asymptotic variance of the estimator. Finally, letting k depend upon n, we see that the resulting estimator has a better finite-sample behavior as well as a better asymptotic efficiency than the refined Pickands estimator.

Sequential Design of Inspection Times in Optimally Spaced Inspection

  • Park San-Gun;Kim Hyun-Joong;Lim Jong-Gun
    • Communications for Statistical Applications and Methods
    • /
    • v.13 no.1
    • /
    • pp.11-17
    • /
    • 2006
  • The spacing of inspection times in intermittent inspection is of great interest, and several ways for the determination of inspection times have been proposed. In most inspection schemes including equally spaced inspection and optimally spaced inspection, the best inspection times in each inspection scheme depend on the unknown parameter, and we need an initial guess of the unknown parameter for practical use. Thus it is evident that the efficiency of the resulting inspection scheme highly depends on the choice of the initial value. However, since we can obtain some information about the unknown parameter at each inspection, we may use the accumulated information and adjust the next inspection time. In this paper, we study this sequential determination of the inspection times in optimally spaced inspection.

Regression Analysis of Longitudinal Data Based on M-estimates

  • Jung, Sin-Ho;Terry M. Therneau
    • Journal of the Korean Statistical Society
    • /
    • v.29 no.2
    • /
    • pp.201-217
    • /
    • 2000
  • The method of generalized estimating equations (GEE) has become very popular for the analysis of longitudinal data. We extend this work to the use of M-estimators; the resultant regression estimates are robust to heavy tailed errors and to outliers. The proposed method does not require correct specification of the dependence structure between observation, and allows for heterogeneity of the error. However, an estimate of the dependence structure may be incorporated, and if it is correct this guarantees a higher efficiency for the regression estimators. A goodness-of-fit test for checking the adequacy of the assumed M-estimation regression model is also provided. Simulation studies are conducted to show the finite-sample performance of the new methods. The proposed methods are applied to a real-life data set.

  • PDF