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Journal articles on the topic 'Non-parametric estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Adhya, Sumanta. "Bootstrap Variance Estimation for Semiparametric Finite Population Distribution Function Estimator." Calcutta Statistical Association Bulletin 70, no. 1 (2018): 17–32. http://dx.doi.org/10.1177/0008068318765583.

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Estimating finite population distribution function (FPDF) emerges as an important problem to the survey statisticians since the pioneering work of Chambers and Dunstan [1] . It unifies estimation of standard finite population parameters, namely, mean and quantiles. Regarding this, estimating variance of FPDF estimator is an important task for accessing the quality of the estimtor and drawing inferences (e.g., confidence interval estimation) on finite population parameters. Due to non-linearity of FPDF estimator, resampling-based methods are developed earlier for parametric or non-parametric Ch

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2

Elston, D. A., C. A. Glasbey, and D. R. Neilson. "Non-parametric lactation curves." Animal Science 48, no. 2 (1989): 331–39. http://dx.doi.org/10.1017/s0003356100040320.

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ABSTRACTLactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates

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3

Jadhav, Deepak, and T. V. Ramanathan. "Parametric and non-parametric estimation of value-at-risk." Journal of Risk Model Validation 3, no. 1 (2009): 51–71. http://dx.doi.org/10.21314/jrmv.2009.034.

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4

Wang, Yong, and Chew-Seng Chee. "Density estimation using non-parametric and semi-parametric mixtures." Statistical Modelling: An International Journal 12, no. 1 (2012): 67–92. http://dx.doi.org/10.1177/1471082x1001200104.

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5

Bayram, Mustafa, Buyukoz Orucova, and Tugcem Partal. "Parameter estimation in a Black Scholes." Thermal Science 22, Suppl. 1 (2018): 117–22. http://dx.doi.org/10.2298/tsci170915277b.

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In this paper we discuss parameter estimation in black scholes model. A non-parametric estimation method and well known maximum likelihood estimator are considered. Our aim is to estimate the unknown parameters for stochastic differential equation with discrete time observation data. In simulation study we compare the non-parametric method with maximum likelihood method using stochastic numerical scheme named with Euler Maruyama.

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6

Zhao, Zhibiao, Yiyun Zhang, and Runze Li. "NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE." Journal of Time Series Analysis 35, no. 1 (2013): 4–15. http://dx.doi.org/10.1111/jtsa.12044.

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7

Sachs, Rainer von. "PEAK-INSENSITIVE NON-PARAMETRIC SPECTRUM ESTIMATION." Journal of Time Series Analysis 15, no. 4 (1994): 429–52. http://dx.doi.org/10.1111/j.1467-9892.1994.tb00203.x.

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8

Ullah, Aman. "Non-Parametric Estimation of Econometric Functionals." Canadian Journal of Economics 21, no. 3 (1988): 625. http://dx.doi.org/10.2307/135443.

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9

Giese, Nadine, Thijs van der Hulst, Paolo Serra, and Tom Oosterloo. "Non-parametric estimation of morphological lopsidedness." Monthly Notices of the Royal Astronomical Society 461, no. 2 (2016): 1656–73. http://dx.doi.org/10.1093/mnras/stw1426.

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10

Randal 3, John A., Peter J. Thomson, and Martin T. Lally. "Non-parametric estimation of historical volatility." Quantitative Finance 4, no. 4 (2004): 427–40. http://dx.doi.org/10.1080/14697680400008692.

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11

Bordes, Laurent. "Non-parametric estimation under progressive censoring." Journal of Statistical Planning and Inference 119, no. 1 (2004): 171–89. http://dx.doi.org/10.1016/s0378-3758(02)00414-7.

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12

SCHMIDT, RAFAEL, and ULRICH STADTMULLER. "Non-parametric Estimation of Tail Dependence." Scandinavian Journal of Statistics 33, no. 2 (2006): 307–35. http://dx.doi.org/10.1111/j.1467-9469.2005.00483.x.

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13

Atto, Abdourrahmane M., Dominique Pastor, and Gregoire Mercier. "Detection threshold for non-parametric estimation." Signal, Image and Video Processing 2, no. 3 (2008): 207–23. http://dx.doi.org/10.1007/s11760-008-0051-x.

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14

Samanta, M. "Non-parametric estimation of conditional quantiles." Statistics & Probability Letters 7, no. 5 (1989): 407–12. http://dx.doi.org/10.1016/0167-7152(89)90095-3.

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15

Fadda, D., E. Slezak, and A. Bijaoui. "Density estimation with non–parametric methods." Astronomy and Astrophysics Supplement Series 127, no. 2 (1998): 335–52. http://dx.doi.org/10.1051/aas:1998355.

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16

Abdulsamad Habeeb *, Ahmed, and Qutaiba N. Nayef Al-Kazaz. "A comparison between Speckman and Bayesian estimation method of a semiparametric balanced longitudinal data model." Journal of Economics and Administrative Sciences 30, no. 142 (2024): 449–64. http://dx.doi.org/10.33095/kpscqv37.

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This paper aims to use semi-parametric regression to balanced longitudinal data model, where the parametric regression models suffer from the problem of strict constraints, while non-parametric regression models, despite their flexibility, suffer from the problem of the curse of dimensionality. Consequently, semi-parametric regression is an ideal solution to get rid of the problems that parametric and non-parametric regression suffer from. The great advantage of this model is that it contains all the positive features included in the previous two models, such as containing strict restrictions

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17

Levin, Andrew, and Wouter J. Den Haan. "Inferences From Parametric and Non-Parametric Covariance Matrix Estimation Procedures." International Finance Discussion Paper 1995, no. 504 (1995): 1–45. http://dx.doi.org/10.17016/ifdp.1995.504.

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18

Gierz, Kristine, Kayoung Park, and Peihua Qiu. "Non-parametric treatment time-lag effect estimation." Statistical Methods in Medical Research 31, no. 1 (2021): 62–75. http://dx.doi.org/10.1177/09622802211032693.

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In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In

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19

LI, R., J. ZHOU, and L. WANG. "ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING." Latin American Applied Research - An international journal 48, no. 3 (2018): 199–204. http://dx.doi.org/10.52292/j.laar.2018.228.

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In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the max

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20

Habeeb, Ahmed Abdulsamad, and Qutaiba N. Nayef Al-Kazaz. "Bayesian and Classical Semi-parametric Estimation of the Balanced Longitudinal Data Model." International Academic Journal of Social Sciences 10, no. 2 (2023): 25–38. http://dx.doi.org/10.9756/iajss/v10i2/iajss1010.

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The primary objective of this study is to employ semi-parametric regression techniques in the balanced longitudinal data model. Where the parametric regression models are plagued by the problem of strict constraints, while non-parametric regression models, despite their flexibility, suffer from the problem of the curse of dimensionality. Consequently, semi-parametric regression presents a suitable solution to address the problems in parametric and non-parametric regression methods. The advantage of this model is that it contains all the positive properties included in the previous two models s

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21

Kattumannil, Sudheesh K., and Sreedevi E.P. "Non-parametric estimation of cumulative (residual) extropy." Statistics & Probability Letters 185 (June 2022): 109434. http://dx.doi.org/10.1016/j.spl.2022.109434.

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22

Samanta, M., and A. Thavaneswaran. "Non-parametric estimation of the conditional mode." Communications in Statistics - Theory and Methods 19, no. 12 (1990): 4515–24. http://dx.doi.org/10.1080/03610929008830455.

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23

Jochmans, Koen. "Pairwise-comparison estimation with non-parametric controls." Econometrics Journal 16, no. 3 (2013): 340–72. http://dx.doi.org/10.1111/ectj.12008.

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24

ALTISSIMO, FILIPPO, and ANTONIO MELE. "Simulated Non-Parametric Estimation of Dynamic Models." Review of Economic Studies 76, no. 2 (2009): 413–50. http://dx.doi.org/10.1111/j.1467-937x.2008.00527.x.

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25

Gribkova, Svetlana, and Olivier Lopez. "Non-parametric Copula Estimation Under Bivariate Censoring." Scandinavian Journal of Statistics 42, no. 4 (2015): 925–46. http://dx.doi.org/10.1111/sjos.12144.

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26

Song, Qiongxia. "Non parametric derivative estimation with confidence bands." Communications in Statistics - Theory and Methods 45, no. 2 (2013): 277–90. http://dx.doi.org/10.1080/03610926.2013.830749.

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27

Arfi, Mounir. "Non‐parametric Variance Estimation from Ergodic Samples." Scandinavian Journal of Statistics 25, no. 1 (1998): 225–34. http://dx.doi.org/10.1111/1467-9469.00099.

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28

Akritas, Michael G., and Ingrid Van Keilegom. "Non-parametric Estimation of the Residual Distribution." Scandinavian Journal of Statistics 28, no. 3 (2001): 549–67. http://dx.doi.org/10.1111/1467-9469.00254.

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29

Gomez, Guadalupe, and M. Luz Calle. "Non-parametric estimation with doubly censored data." Journal of Applied Statistics 26, no. 1 (1999): 45–58. http://dx.doi.org/10.1080/02664769922647.

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30

Abi-ayad, Ilham, and Tahar Mourid. "Parametric estimation for non recurrent diffusion processes." Statistics & Probability Letters 141 (October 2018): 96–102. http://dx.doi.org/10.1016/j.spl.2018.05.024.

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31

El-Gamal, Mahmoud A. "Non-parametric estimation of deterministically chaotic systems." Economic Theory 1, no. 2 (1991): 147–67. http://dx.doi.org/10.1007/bf01211531.

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32

Chiang, Chin-Tsang, and Hung Hung. "Non‐parametric estimation for time-dependent AUC." Journal of Statistical Planning and Inference 140, no. 5 (2010): 1162–74. http://dx.doi.org/10.1016/j.jspi.2009.10.012.

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33

Krzanowski, W. J. "Non-parametric estimation of distance between groups." Journal of Applied Statistics 30, no. 7 (2003): 743–50. http://dx.doi.org/10.1080/0266476032000076029.

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34

Brendstrup, Bjarne. "Non-parametric estimation of sequential english auctions." Journal of Econometrics 141, no. 2 (2007): 460–81. http://dx.doi.org/10.1016/j.jeconom.2006.10.004.

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35

Poznyak, A. S., and M. V. Bondarenko. "Parametric Estimation in Non Stationary Armax-Systems." IFAC Proceedings Volumes 27, no. 8 (1994): 1345–50. http://dx.doi.org/10.1016/s1474-6670(17)47897-9.

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36

Genxiang, Chai, Sun Yan, and Yang Xiaohan. "Non-parametric estimation in contaminated linear model." Applied Mathematics-A Journal of Chinese Universities 16, no. 2 (2001): 195–202. http://dx.doi.org/10.1007/s11766-001-0027-x.

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37

Jiang, Honghua, James Symanowski, Yongming Qu, Xiao Ni, and Yanping Wang. "Covariate-adjusted non-parametric survival curve estimation." Statistics in Medicine 30, no. 11 (2011): 1243–53. http://dx.doi.org/10.1002/sim.4216.

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38

Eğecioğlu, Ömer, and Ashok Srinivasan. "A Fast Non-Parametric Density Estimation Algorithm." Communications in Numerical Methods in Engineering 13, no. 10 (1997): 755–63. http://dx.doi.org/10.1002/(sici)1099-0887(199710)13:10<755::aid-cnm88>3.0.co;2-a.

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39

Agrez, Dusan. "Estimation of the signal component from random equivalent and non-coherent sampling measurements." ACTA IMEKO 6, no. 4 (2017): 54. http://dx.doi.org/10.21014/acta_imeko.v6i4.474.

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Estimations of the signal component parameters in the case of random equivalent time sampling and under non-coherent sampling condition comprise two main error contributions: spectral leakage effect due to non-coherency and additional noise due to the randomization of sampling intervals. In the estimation procedure the non-parametric interpolated DFT approach has to be used first to estimate the component frequency and, after that, an iterative 4-parametric sine-fit algorithm should be used for other component parameters (amplitude and phase). Their estimations are possible when the duty ratio

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40

Witkovský, Viktor, Gejza Wimmer, and Tomas Duby. "Estimating the distribution of a stochastic sum of IID random variables." Mathematica Slovaca 70, no. 3 (2020): 759–74. http://dx.doi.org/10.1515/ms-2017-0389.

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AbstractSuggested is a non-parametric method and algorithm for estimating the probability distribution of a stochastic sum of independent identically distributed continuous random variables, based on combining and numerically inverting the associated empirical characteristic function (CF) derived from the observed data. This is motivated by classical problems in financial risk management, actuarial science, and hydrological modelling. This approach can be naturally generalized to more complex semi-parametric modelling and estimating approaches, e.g., by incorporating the generalized Pareto dis

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41

Huang, Tai-Hsin, and Mei-Hui Wang. "Comparison of Economic Efficiency Estimation Methods: Parametric and Non-parametric Techniques." Manchester School 70, no. 5 (2002): 682–709. http://dx.doi.org/10.1111/1467-9957.00320.

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42

Yakowitz, S. "Parametric and non-parametric density estimation to account for extreme events." Advances in Applied Probability 20, no. 1 (1988): 13. http://dx.doi.org/10.1017/s0001867800017912.

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43

Villarini, Gabriele, Francesco Serinaldi, and Witold F. Krajewski. "Modeling radar-rainfall estimation uncertainties using parametric and non-parametric approaches." Advances in Water Resources 31, no. 12 (2008): 1674–86. http://dx.doi.org/10.1016/j.advwatres.2008.08.002.

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44

Liu, Wei, and R. Doraiswami. "Robust high resolution spectral estimation: a combined non-parametric–parametric approach." Journal of the Franklin Institute 336, no. 1 (1999): 159–84. http://dx.doi.org/10.1016/s0016-0032(97)00051-3.

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45

Kiderlen, Markus. "Non-parametric estimation of the directional distribution of stationary line and fibre processes." Advances in Applied Probability 33, no. 1 (2001): 6–24. http://dx.doi.org/10.1017/s0001867800010600.

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Two non-parametric methods for the estimation of the directional measure of stationary line and fibre processes in d-dimensional space are presented. The input data for both methods are intersection counts with finitely many test windows situated in hyperplanes. The first estimator is a measure valued maximum likelihood estimator, if applied to Poisson line processes. The second estimator uses an approximation of the associated zonoid (the Steiner compact) by zonotopes. Consistency of both estimators is proved (without use of the Poisson assumption). The estimation methods are compared empiric

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46

Prasetyo, Hanung, Ferra Arik Tridalestari, and Wawa Wikusna. "IMPLEMENTATION OF SURVIVAL ESTIMATION OF BONE MARROW TRANSPLANT PATIENTS WITH SEMIPARAMETRIC HAZARD FUNCTION USING MINITAB SOFTWARE." Jurnal Teknik Informatika (Jutif) 3, no. 5 (2022): 1177–82. http://dx.doi.org/10.20884/1.jutif.2022.3.5.280.

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The Hazard Rate probability value estimation method is an estimation model that is carried out fully parametrically or it can also be done by non-parametric methods. Sometimes using the parametric method will give biased value results because it gives too much value in general, while the non-parametric estimation method causes the variance value to be too high. Therefore, for some cases there is a way to combine the two methods, which is called the Semiparametric method, which is an estimation method that has the characteristics of improving non-parametric parametric estimates. This paper show

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47

Situmeang, Yohanes Gladser, and Sutarman. "Estimation of Non-Parametric Regression Parameters Using Bootstrap." Journal of Mathematics Technology and Education 2, no. 2 (2023): 162–77. https://doi.org/10.32734/jomte.v2i2.9715.

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This study aims to estimate non-parametric regression parameters using a spline truncated approach. The approach used is the Ordinary Least Square method and the Bootstrap method. The estimation results are then compared to find out the best non-parametric regression model. The knot points used are 1 knot, 2 knots and 3 knots. Based on the discussion conducted, it is obtained that parameter estimation and non-parametric spline truncated regression model using the Bootstrap method are better than the OLS method. This is because the estimation using the Bootstrap method has a smaller GCV at each

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48

Kh.Bahez, Zahraa, and Husam .A. Rasheed. "Comparing Some of Robust the Non-Parametric Methods for Semi-Parametric Regression Models Estimation." Journal of Economics and Administrative Sciences 28, no. 132 (2022): 105–17. http://dx.doi.org/10.33095/jeas.v28i132.2275.

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In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing. The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small

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49

Ruzgas, T., R. Rudzkis, and M. Kavaliauskas. "Application of Clustering in the Non-Parametric Estimation of Distribution Density." Nonlinear Analysis: Modelling and Control 11, no. 4 (2006): 393–411. http://dx.doi.org/10.15388/na.2006.11.4.14741.

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This paper discusses a multimodal density function estimation problem of a random vector. A comparative accuracy analysis of some popular non-parametric estimators is made by using the Monte-Carlo method. The paper demonstrates that the estimation quality increases significantly if the sample is clustered (i.e., the multimodal density function is approximated by a mixture of unimodal densities), and later on, the density estimation methods are applied separately to each cluster. In this paper, the sample is clustered using the Gaussian distribution mixture model and the EM algorithm. The highe

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50

Hadj-Rabah, Karima, Gilda Schirinzi, Alessandra Budillon, Faiza Hocine, and Aichouche Belhadj-Aissa. "Non-Parametric Tomographic SAR Reconstruction via Improved Regularized MUSIC." Remote Sensing 15, no. 6 (2023): 1599. http://dx.doi.org/10.3390/rs15061599.

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Height estimation of scatterers in complex environments via the Tomographic Synthetic Aperture Radar (TomoSAR) technique is still a valuable research field. The parametric spectral estimation approach constitutes a powerful tool to identify the superimposed scatterers with different complex reflectivities, located at different heights in the same range–azimuth resolution cell. Unfortunately, this approach requires prior knowledge about the number of scatterers for each pixel, which is not possible in practical situations. In this paper, we propose a method that analyzes the scree plot, generat

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