• 제목/요약/키워드: linear estimator

검색결과 391건 처리시간 0.028초

Modified Local Density Estimation for the Log-Linear Density

  • Pak, Ro-Jin
    • Communications for Statistical Applications and Methods
    • /
    • 제7권1호
    • /
    • pp.13-22
    • /
    • 2000
  • We consider local likelihood method with a smoothed version of the model density in stead of an original model density. For simplicity a model is assumed as the log-linear density then we were able to show that the proposed local density estimator is less affected by changes among observations but its bias increases little bit more than that of the currently used local density estimator. Hence if we use the existing method and the proposed method in a proper way we would derive the local density estimator fitting the data in a better way.

  • PDF

SMOOTH NONPARAMETRIC ESTIMATION OF MEAN RESIDUAL LIFE

  • Na, Myoung-Hwan;Park, Sung-Hyun;Kim, Jae-Joo
    • 한국품질경영학회:학술대회논문집
    • /
    • 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
    • /
    • pp.571-579
    • /
    • 1998
  • In this paper we propose smooth nonparametric estimator of Mean Residual Life(MRL) based on a complete sample. This estimator is constructed using estimator of cumulative failure rate which is derived as the maximum likelihood estimate of cumulative failure rate in the class of distributions which have piecewise linear failure rate functions between each pair of observations. We derive the asymptotic properties of the our estimator. The proposed estimator is compared with previously known estimator by Monte Carlo study.

  • PDF

A Non-Linear Exponential(NLINEX) Loss Function in Bayesian Analysis

  • Islam, A.F.M.Saiful;Roy, M.K.;Ali, M.Masoom
    • Journal of the Korean Data and Information Science Society
    • /
    • 제15권4호
    • /
    • pp.899-910
    • /
    • 2004
  • In this paper we have proposed a new loss function, namely, non-linear exponential(NLINEX) loss function, which is quite asymmetric in nature. We obtained the Bayes estimator under exponential(LINEX) and squared error(SE) loss functions. Moreover, a numerical comparison among the Bayes estimators of power function distribution under SE, LINEX, and NLINEX loss function have been made.

  • PDF

Improved Single-Tone Frequency Estimation by Averaging and Weighted Linear Prediction

  • So, Hing Cheung;Liu, Hongqing
    • ETRI Journal
    • /
    • 제33권1호
    • /
    • pp.27-31
    • /
    • 2011
  • This paper addresses estimating the frequency of a cisoid in the presence of white Gaussian noise, which has numerous applications in communications, radar, sonar, and instrumentation and measurement. Due to the nonlinear nature of the frequency estimation problem, there is threshold effect, that is, large error estimates or outliers will occur at sufficiently low signal-to-noise ratio (SNR) conditions. Utilizing the ideas of averaging to increase SNR and weighted linear prediction, an optimal frequency estimator with smaller threshold SNR is developed. Computer simulations are included to compare its mean square error performance with that of the maximum likelihood (ML) estimator, improved weighted phase averager, generalized weighted linear predictor, and single weighted sample correlator as well as Cramer-Rao lower bound. In particular, with smaller computational requirement, the proposed estimator can achieve the same threshold and estimation performance of the ML method.

Pitman Nearness for a Generalized Stein-Rule Estimators of Regression Coefficients

  • R. Karan Singh;N. Rastogi
    • Journal of the Korean Statistical Society
    • /
    • 제31권2호
    • /
    • pp.229-235
    • /
    • 2002
  • A generalized Stein-rule estimator of the vector of regression coefficients in linear regression model is considered and its properties are analyzed according to the criterion of Pitman nearness. A comparative study shows that the generalized Stein-rule estimator representing a class of estimators contains particular members which are better than the usual Stein-rule estimator according to the Pitman closeness.

Nonparametric Reliability Estimation in Strength-Stress Model: B-Spline Approach

  • Kim, Jae-Joo;Na, Myung-Hwan;Lee, Kang-Hyun
    • 품질경영학회지
    • /
    • 제27권2호
    • /
    • pp.152-162
    • /
    • 1999
  • In this paper we develope a new nonparametric estimator of the reliability in strength-stress model. This estimator is constructed using the maximum likelihood estimate of cumulative failure rate in the class of distributions which have piecewise linear failure rate functions between each pair of observations. Large sample properties of our estimator are examined. The proposed estimator is compared with previously known estimator by Monte Carlo study.

  • PDF

Local Influence of the Quasi-likelihood Estimators in Generalized Linear Models

  • Jung, Kang-Mo
    • Communications for Statistical Applications and Methods
    • /
    • 제14권1호
    • /
    • pp.229-239
    • /
    • 2007
  • We present a diagnostic method for the quasi-likelihood estimators in generalized linear models. Since these estimators can be usually obtained by iteratively reweighted least squares which are well known to be very sensitive to unusual data, a diagnostic step is indispensable to analysis of data. We extend the local influence approach based on the maximum likelihood function to that on the quasi-likelihood function. Under several perturbation schemes local influence diagnostics are derived. An illustrative example is given and we compare the results provided by local influence and deletion.

A Comparison of Distribution-free Two-sample Procedures Based on Placements or Ranks

  • Kim, Dong-Jae
    • Journal of the Korean Statistical Society
    • /
    • 제23권1호
    • /
    • pp.135-149
    • /
    • 1994
  • We discussed a comparison of distribution-free two-sample procedures based on placements or ranks. Iterative asymptotic distribution of both two-sample procedures is studies and small sample Monte Carlo simulation results are presented. Also, we proposed the Hodges-Lehmann type location estimator based on linear placement statistics.

  • PDF