• Title/Summary/Keyword: Nonparametric

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Power Analysis of Distributions between Nonparametric Tests

  • Chan Keun Park
    • Communications for Statistical Applications and Methods
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    • v.5 no.2
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    • pp.417-429
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    • 1998
  • This paper compares powers of the two nonparametric tests under a variety of population distributions through a simulation study. Both tests require that the two underlying populations have the same variance, but this assumption is relaxed in some of the comparisons.

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Some Nonparametric Tests for Change-points with Epidemic Alternatives

  • Kim, Kyung-Moo
    • Communications for Statistical Applications and Methods
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    • v.4 no.2
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    • pp.427-434
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    • 1997
  • The purpose of this paper is to discuss distribution-free tests of hypothesis that the random samples are identically distributed against the epidemic alternative. But most tests that have been considered are depended only on specific null distribution. Two nonparametric tests are considered and compared with a likelihood ratio test by the empirical powers.

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Diagnostic for Smoothing Parameter Estimate in Nonparametric Regression Model

  • In-Suk Lee;Won-Tae Jung
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.266-276
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    • 1995
  • We have considered the study of local influence for smoothing parameter estimates in nonparametric regression model. Practically, generalized cross validation(GCV) does not work well in the presence of data perturbation. Thus we have proposed local influence measures for GCV estimates and examined effects of diagnostic by above measures.

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On a Transformation Technique for Nonparametric Regression

  • Kim, Woochul;Park, Byeong U.
    • Journal of the Korean Statistical Society
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    • v.25 no.2
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    • pp.217-233
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    • 1996
  • This paper gives a rigorous proof of an asymptotic result about bias and variance for a transformation-based nonparametric regression estimator proposed by Park et al (1995).

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Nonparametric Regression with Left-Truncated and Right-Censored Data

  • Park, Jinho
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.791-800
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    • 1999
  • Gross and Lai(1996) proposed a new approach for ordinary regression with left-truncated and right-censored (I.t.r.c) data. This paper shows how to apply nonparametric algorithms such as multivariate adaptive regression splines to 1.t.r.c data.

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Nonparametric procedures using aligned method and linear placement statistics in randomized block design (랜덤화 블록 계획법에서 정렬방법과 선형위치통계량을 이용한 비모수 검정법)

  • Han, Jinjoo;Kim, Dongjae
    • The Korean Journal of Applied Statistics
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    • v.29 no.7
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    • pp.1411-1419
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    • 2016
  • Nonparametric procedures in randomized block design was proposed by Friedman (1937) as a general alternative. This method is used to find out the difference in treatment effect. It can cause a loss of inter block information using the ranking in each block. This paper proposed nonparametric procedures using an aligned method proposed by Hodges and Lehmann (1962) to reduce block information based on joint placement suggest by Jo and Kim (2013) in a randomized block design. We also compared the power of the test of the proposed procedures and established method through a Monte Carlo simulation.

On Employing Nonparametric Bootstrap Technique in Oscillometric Blood Pressure Measurement for Confidence Interval Estimation

  • Lee, Yong-Kook;Lee, Im-Bong;Chang, Joon-Hyuk;Lee, Soo-Jeong
    • Journal of Korea Multimedia Society
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    • v.17 no.2
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    • pp.200-207
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    • 2014
  • Blood pressure (BP) is an important vital signal for determining the health of an individual subject. Although estimation of mean arterial blood pressure is possible using oscillometric blood pressure techniques, there are no established techniques in the literature for obtaining confidence interval (CI) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates obtained from such BP measurements. This paper proposes a nonparametric bootstrap technique to obtain CI with a small number of the BP measurements. The proposed algorithm uses pseudo measurements employing nonparametric bootstrap technique to derive the pseudo maximum amplitudes (PMA) and the pseudo envelopes (PE). The SBP and DBP are then derived using the new relationships between PMA and PE and the CIs for such estimates. Application of the proposed method on an experimental dataset of 85 patients with five sets of measurements for each patient has yielded a smaller Cl than the conventional student t-method.