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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Bukac, Josef. "Weighted nonlinear regression." Analysis in Theory and Applications 24, no. 4 (December 2008): 330–35. http://dx.doi.org/10.1007/s10496-008-0330-y.

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2

Verboon, Peter. "Robust nonlinear regression analysis." British Journal of Mathematical and Statistical Psychology 46, no. 1 (May 1993): 77–94. http://dx.doi.org/10.1111/j.2044-8317.1993.tb01003.x.

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3

Ng, Meei Pyng, and Gary K. Grunwald. "Nonlinear Regression Analysis of the Joint-Regression Model." Biometrics 53, no. 4 (December 1997): 1366. http://dx.doi.org/10.2307/2533503.

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4

Kass, Robert E., Douglas M. Bates, Donald G. Watts, G. A. F. Seber, and C. J. Wild. "Nonlinear Regression Analysis and Its Applications." Journal of the American Statistical Association 85, no. 410 (June 1990): 594. http://dx.doi.org/10.2307/2289810.

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5

Howell, Roy D., Douglas M. Bates, and Donald G. Watts. "Nonlinear Regression Analysis & Its Application." Journal of Marketing Research 27, no. 1 (February 1990): 113. http://dx.doi.org/10.2307/3172558.

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6

Hung, Hsien-Ming. "Nonlinear regression analysis for complex surveys1." Communications in Statistics - Theory and Methods 19, no. 9 (January 1990): 3447–70. http://dx.doi.org/10.1080/03610929008830390.

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7

Slepicka, James S., and Soyoung S. Cha. "Stabilized nonlinear regression for interferogram analysis." Applied Optics 34, no. 23 (August 10, 1995): 5039. http://dx.doi.org/10.1364/ao.34.005039.

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8

Milliken, George A. "Nonlinear Regression Analysis and Its Applications." Technometrics 32, no. 2 (May 1990): 219–20. http://dx.doi.org/10.1080/00401706.1990.10484638.

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9

Efremov, G. I., T. Yu Zhuravleva, and B. S. Sazhin. "Data processing by nonlinear regression analysis." Theoretical Foundations of Chemical Engineering 34, no. 2 (March 2000): 194–96. http://dx.doi.org/10.1007/bf02757840.

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10

Ye, Ya-Fen, Chao Ying, Yuan-Hai Shao, Chun-Na Li, and Yu-Juan Chen. "Robust and SparseLP-Norm Support Vector Regression." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 6 (October 20, 2017): 989–97. http://dx.doi.org/10.20965/jaciii.2017.p0989.

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Анотація:

A robust and sparseLp-norm support vector regression (Lp-RSVR) is proposed in this paper. The implementation of feature selection in ourLp-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics ofLp-RSVR are as follows: (i) By using the absolute constraint,Lp-RSVR performs robustly against outliers. (ii)Lp-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinearLp-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposedLp-RSVR in both feature selection and regression performance as well as its robustness.

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11

Schmidt, Wolfgang H., and S. Zwanzig. "Second order asymptotics in nonlinear regression." Journal of Multivariate Analysis 18, no. 2 (April 1986): 187–215. http://dx.doi.org/10.1016/0047-259x(86)90069-2.

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12

Kniss, Andrew R., Joseph D. Vassios, Scott J. Nissen, and Christian Ritz. "Nonlinear Regression Analysis of Herbicide Absorption Studies." Weed Science 59, no. 4 (December 2011): 601–10. http://dx.doi.org/10.1614/ws-d-11-00034.1.

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Анотація:

Although foliar herbicide absorption has been studied intensively, there is currently no standardized method for data analysis when evaluating herbicide absorption over time. Most peer-reviewed journals require the treatment structure of data be incorporated in the analysis; however, many herbicide absorption studies published in the past 5 yr do not account for the time structure of the experiment. Herbicide absorption studies have been presented in a variety of ways, making it difficult to compare results among studies. The objective of this article is to propose possible nonlinear models to analyze herbicide absorption data and to provide a stepwise framework so that researchers may standardize the analysis method in this important research area. Asymptotic regression and rectangular hyperbolic models with similar parameterizations are proposed, so that the maximum herbicide absorption and absorption rate may be adequately modeled and statistically compared among treatments. Adoption of these models for herbicide absorption analysis over time will provide a standardized method making comparison of results within and among studies more practical.

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13

Suits, L. D., T. C. Sheahan, Pérsio L. A. Barros, and Paulo R. O. Pinto. "Oedometer Consolidation Test Analysis by Nonlinear Regression." Geotechnical Testing Journal 31, no. 1 (2008): 101007. http://dx.doi.org/10.1520/gtj101007.

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14

Ward, B. D., H. Garavan, T. J. Ross, A. S. Bloom, R. W. Cox, and E. A. Stein. "Nonlinear Regression for FMRI Time Series Analysis." NeuroImage 7, no. 4 (May 1998): S767. http://dx.doi.org/10.1016/s1053-8119(18)31600-8.

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15

Chen, Jia, and Junke Kou. "Nonparametric Pointwise Estimation for a Regression Model with Multiplicative Noise." Journal of Function Spaces 2021 (October 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/1599286.

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Анотація:

In this paper, we consider a general nonparametric regression estimation model with the feature of having multiplicative noise. We propose a linear estimator and nonlinear estimator by wavelet method. The convergence rates of those regression estimators under pointwise error over Besov spaces are proved. It turns out that the obtained convergence rates are consistent with the optimal convergence rate of pointwise nonparametric functional estimation.

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16

Wu, Qiang, Feng Liang, and Sayan Mukherjee. "Kernel Sliced Inverse Regression: Regularization and Consistency." Abstract and Applied Analysis 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/540725.

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Kernel sliced inverse regression (KSIR) is a natural framework for nonlinear dimension reduction using the mapping induced by kernels. However, there are numeric, algorithmic, and conceptual subtleties in making the method robust and consistent. We apply two types of regularization in this framework to address computational stability and generalization performance. We also provide an interpretation of the algorithm and prove consistency. The utility of this approach is illustrated on simulated and real data.

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17

Jarriel, W. Scott, Peter Richardson, Roger D. Knapp, and Thomas N. Hansen. "A nonlinear regression analysis of nonlinear, passive-deflation flow-volume plots." Pediatric Pulmonology 15, no. 3 (March 1993): 175–82. http://dx.doi.org/10.1002/ppul.1950150309.

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18

Jēkabsons, Gints, Jurijs Lavendels, and Vjaceslavs Sitikovs. "MODEL EVALUATION AND SELECTION IN MULTIPLE NONLINEAR REGRESSION ANALYSIS." Mathematical Modelling and Analysis 12, no. 1 (March 31, 2007): 81–90. http://dx.doi.org/10.3846/1392-6292.2007.12.81-90.

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Анотація:

The main problem in regression model selection is finding the best model that best fits the data, i.e. it does not neither overfit nor underfit. The aim of this work is to show one of possible ways to find adequate nonlinear regression models (parametric) of technical systems based on an heuristic search and analytical optimality evaluation approach by taking into consideration the computational power of modern computers.

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19

NASRABADI, EBRAHIM, and S. MEHDI HASHEMI. "ROBUST FUZZY REGRESSION ANALYSIS USING NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 579–98. http://dx.doi.org/10.1142/s021848850800542x.

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Some neural network related methods have been applied to nonlinear fuzzy regression analysis by several investigators. The performance of these methods will significantly worsen when the outliers exist in the training data set. In this paper, we propose a training algorithm for fuzzy neural networks with general fuzzy number weights, biases, inputs and outputs for computation of nonlinear fuzzy regression models. First, we define a cost function that is based on the concept of possibility of fuzzy equality between the fuzzy output of fuzzy neural network and the corresponding fuzzy target. Next, a training algorithm is derived from the cost function in a similar manner as the back-propagation algorithm. Last, we examine the ability of our approach by computer simulations on numerical examples. Simulation results show that the proposed algorithm is able to reduce the outlier effects.

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20

Krzyżak, Adam, and Marian A. Partyka. "Nonparametric estimation of nonlinear dynamic systems using semirecursive regression estimates." Nonlinear Analysis: Theory, Methods & Applications 71, no. 12 (December 2009): e1942-e1951. http://dx.doi.org/10.1016/j.na.2009.02.128.

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21

Yoshihara, Ken-ichi, and Shuya Kanagawa. "Change-point problems in nonlinear regression estimation with dependent observations." Nonlinear Analysis: Theory, Methods & Applications 71, no. 12 (December 2009): e2152-e2163. http://dx.doi.org/10.1016/j.na.2009.04.016.

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22

Fitzpatrick, B. G., and G. Yin. "Large Sample Behavior in Bayesian Analysis of Nonlinear Regression Models." Journal of Mathematical Analysis and Applications 192, no. 2 (June 1995): 607–26. http://dx.doi.org/10.1006/jmaa.1995.1192.

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23

Yang, Nan, Dawei Zhang, and Yanling Tian. "The Validity Analysis of Regression: Combining Uniform Experiment Design with Nonlinear Regression." Applied Mathematics 06, no. 06 (2015): 996–1008. http://dx.doi.org/10.4236/am.2015.66092.

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24

Peddada, Shyamal D., and Joseph K. Haseman. "Analysis of Nonlinear Regression Models: A Cautionary Note." Dose-Response 3, no. 3 (May 1, 2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.03.005.

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Анотація:

Regression models are routinely used in many applied sciences for describing the relationship between a response variable and an independent variable. Statistical inferences on the regression parameters are often performed using the maximum likelihood estimators (MLE). In the case of nonlinear models the standard errors of MLE are often obtained by linearizing the nonlinear function around the true parameter and by appealing to large sample theory. In this article we demonstrate, through computer simulations, that the resulting asymptotic Wald confidence intervals cannot be trusted to achieve the desired confidence levels. Sometimes they could underestimate the true nominal level and are thus liberal. Hence one needs to be cautious in using the usual linearized standard errors of MLE and the associated confidence intervals.

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25

Kobayashi, Masahito. "Testing for Autocorrelated Disturbances in Nonlinear Regression Analysis." Econometrica 59, no. 4 (July 1991): 1153. http://dx.doi.org/10.2307/2938178.

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26

Qi, Qing Lan, and Shao Xiong Zhang. "Nonlinear Regression Analysis for Programming and Engineering Application." Advanced Materials Research 846-847 (November 2013): 1080–83. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1080.

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Aiming at the calculating the flow of cutthroat flume in Hydraulic Engineering, the test data is processed through the nonlinear regression analysis program which is based on the principle of least square method. With 33 equations of various functions including linear, power function curve and exponential curve to be selected as the mathematical model, the regression analysis is taken through 33 equations. Comparing the regression coefficient from analysis, the optimal mathematical model is selected as the empirical formula, which has great significance in guiding the practical engineering.

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27

Howell, Roy D. "Book Review: Nonlinear Regression Analysis & its Application." Journal of Marketing Research 27, no. 1 (February 1990): 113–14. http://dx.doi.org/10.1177/002224379002700113.

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28

Speyer, Robert F., Bruce C. Richardson, and Subhash H. Risbud. "Nonlinear regression analysis of superimposed DSC crystallization peaks." Metallurgical Transactions A 17, no. 8 (August 1986): 1479–81. http://dx.doi.org/10.1007/bf02650131.

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29

Csanády, G. A., and J. G. Filser. "Statistical analysis of toxicokinetic data by nonlinear regression." Archives of Toxicology 67, no. 3 (April 1993): 227–30. http://dx.doi.org/10.1007/bf01973313.

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30

Bárdossy, András, István Bogárdi, and Lucien Duckstein. "Fuzzy nonlinear regression analysis of dose-response relationships." European Journal of Operational Research 66, no. 1 (April 1993): 36–51. http://dx.doi.org/10.1016/0377-2217(93)90204-z.

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31

McKeague, Ian W., and Klaus J. Utikal. "Identifying nonlinear covariate effects in semimartingale regression models." Probability Theory and Related Fields 87, no. 1 (March 1990): 1–25. http://dx.doi.org/10.1007/bf01217745.

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32

Achen, Christopher H. "Two-Step Hierarchical Estimation: Beyond Regression Analysis." Political Analysis 13, no. 4 (2005): 447–56. http://dx.doi.org/10.1093/pan/mpi033.

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Анотація:

Two-step estimators for hierarchical models can be constructed even when neither stage is a conventional linear regression model. For example, the first stage might consist of probit models, or duration models, or event count models. The second stage might be a nonlinear regression specification. This note sketches some of the considerations that arise in ensuring that two-step estimators are consistent in such cases.

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33

Prakasa Rao, B. L. S. "Estimation of cusp in nonregular nonlinear regression models." Journal of Multivariate Analysis 88, no. 2 (February 2004): 243–51. http://dx.doi.org/10.1016/s0047-259x(03)00102-7.

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34

Wang, J. D. "Asymptotic Normality of L1-Estimators in Nonlinear Regression." Journal of Multivariate Analysis 54, no. 2 (August 1995): 227–38. http://dx.doi.org/10.1006/jmva.1995.1054.

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35

Zhong, Gao Yan. "Ternary Regression Modeling Analysis of NC Ultrasonic Machining Efficiency." Applied Mechanics and Materials 37-38 (November 2010): 1388–92. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.1388.

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Анотація:

To explore the impact of abrasive granularity, feed pressure and cutting feed speed on NC ultrasonic machining efficiency, a three-factor four-level orthogonal test was carried out, and data were analyzed to establish a ternary nonlinear regression model of NC ultrasonic machining efficiency. Furthermore, significance of the regression model and impact of all independent variables on dependent variables were studied. The study showed that when significance level is α = 0.01, the combination of the three factors tested impacts significantly on machining efficiency indicators. However, among the three factors, the abrasive granularity has the highest impact and its impact on the machining efficiency is nonlinear.

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36

Wu, Jiansheng. "An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression." International Journal of Applied Evolutionary Computation 2, no. 4 (October 2011): 50–65. http://dx.doi.org/10.4018/jaec.2011100104.

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Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

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37

Yoshida, Takuma. "Nonlinear surface regression with dimension reduction method." AStA Advances in Statistical Analysis 101, no. 1 (June 3, 2016): 29–50. http://dx.doi.org/10.1007/s10182-016-0271-2.

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38

Arnhold, Emmanuel. "R-environment package for regression analysis." Pesquisa Agropecuária Brasileira 53, no. 7 (July 2018): 870–73. http://dx.doi.org/10.1590/s0100-204x2018000700012.

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Анотація:

Abstract: The objective of this work was to develop a package in the R environment for automating and facilitating the regression analysis. Named easyreg, the package offers five functions. The er1 function performs analyses in 13 models, including linear, nonlinear, and mixed models. The er2 function considers the lack of fit in the analyses and in the following designs: completely randomized, randomized complete block, Latin squares, and repeated Latin squares. The regplot function generates graphics; the bl function estimates two-segment models; and the regtest function tests the equality of parameters and the identity of the models. These functions allow of a great number of analyses and confer practicality and versatility to the regression analysis.

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39

GUO Lijun, 郭丽君, 郭丹 GUO Dan, 何弼 HE Bi, 杨彤瑶 YANG Tongyao, 许晓平 XU Xiaoping, 谢涛 XIE Tao, 赵振刚 ZHAO Zhengang, 李英娜 LI Yingna, and 李川 LI Chuan. "Nonlinear regression analysis research on tunnel secondary lining monitoring." Optical Technique 40, no. 1 (2014): 66–70. http://dx.doi.org/10.3788/gxjs20144001.0066.

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40

Zhang, Yi, Chun Ru Fu, You Yi Zhu, Qun Zhang, and Jie Wu. "Nonlinear Regression Analysis of Binary Flooding Recovery Influence Factors." Advanced Materials Research 881-883 (January 2014): 1696–705. http://dx.doi.org/10.4028/www.scientific.net/amr.881-883.1696.

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Анотація:

To better reveal the laws of binary (SP) oil flooding, based on the physical simulative experimental results of Berea core (400mD), the nonlinear regression method of the probability statistics theory and SPSS software was applied to conduct a weight analysis for the main influence factors of the binary oil flooding. The results show that the three factors of the viscosity of binary system, the composite emulsifying index and the interfacial tension affect the chemical flooding recovery and the total recovery significantly. The results of the forced introduction method show that the reliability of the effect of the three factors on y1, namely the chemical flooding recovery, is higher than 99%. The stepwise introduction method and the backward elimination method prove that the reliability of the effect of the three factors on y2, namely the total recovery, is higher than 95%. The results also indicate that emulsification affects the binary chemical flooding recovery of moderately and highly penetrated sandstone cores more significantly.Introduction

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41

Kobayashi, Masahito. "Power of Tests for Nonlinear Transformation in Regression Analysis." Econometric Theory 10, no. 2 (June 1994): 357–71. http://dx.doi.org/10.1017/s0266466600008446.

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This paper compares the local power of tests for a nonlinear transformation of the dependent variable in a regression model against the alternative hypothesis of a linear transformation. It is shown that the local power of the Cox test is higher than those of the extended projection test of MacKinnon, White, and Davidson, and Bera and McAleer's test. The theoretical result is supported by a Monte-Carlo experiment in testing for a regression model with a logarithmically transformed dependent variable against a linear regression model.

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42

He, Yu-Lin, Xi-Zhao Wang, and Joshua Zhexue Huang. "Fuzzy nonlinear regression analysis using a random weight network." Information Sciences 364-365 (October 2016): 222–40. http://dx.doi.org/10.1016/j.ins.2016.01.037.

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43

Osiewalski, Jacek. "Bayesian analysis of nonlinear regression with equicorrelated elliptical errors." Test 8, no. 2 (December 1999): 339–44. http://dx.doi.org/10.1007/bf02595874.

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44

Ivanov, A. V., and N. N. Leonenko. "Semiparametric analysis of long-range dependence in nonlinear regression." Journal of Statistical Planning and Inference 138, no. 6 (July 2008): 1733–53. http://dx.doi.org/10.1016/j.jspi.2007.06.027.

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45

Lew, M. "Analysis of competitive agonist-antagonist interactions by nonlinear regression." Trends in Pharmacological Sciences 16, no. 10 (October 1995): 328–37. http://dx.doi.org/10.1016/s0165-6147(00)89066-5.

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46

Bennink, Edwin, Jaap Oosterbroek, Kohsuke Kudo, Max A. Viergever, Birgitta K. Velthuis, and Hugo W. A. M. de Jong. "Fast nonlinear regression method for CT brain perfusion analysis." Journal of Medical Imaging 3, no. 2 (June 16, 2016): 026003. http://dx.doi.org/10.1117/1.jmi.3.2.026003.

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47

Makarenkov, Vladimir, and Pierre Legendre. "NONLINEAR REDUNDANCY ANALYSIS AND CANONICAL CORRESPONDENCE ANALYSIS BASED ON POLYNOMIAL REGRESSION." Ecology 83, no. 4 (April 2002): 1146–61. http://dx.doi.org/10.1890/0012-9658(2002)083[1146:nraacc]2.0.co;2.

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48

Liao, Wenjing, Mauro Maggioni, and Stefano Vigogna. "Multiscale regression on unknown manifolds." Mathematics in Engineering 4, no. 4 (2022): 1–25. http://dx.doi.org/10.3934/mine.2022028.

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Анотація:

<abstract><p>We consider the regression problem of estimating functions on $ \mathbb{R}^D $ but supported on a $ d $-dimensional manifold $ \mathcal{M} ~~\subset \mathbb{R}^D $ with $ d \ll D $. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on $ \mathcal{M} $ at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension $ d $, instead of an unknown manifold embedded in $ \mathbb{R}^D $. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in $ D $ and exponential in $ d $. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.</p></abstract>

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49

Prahutama, Alan, Budi Warsito, and Moch Abdul Mukid. "ANALYSIS OF THE NUMBER INFANT AND MATERNAL MORTALITY IN CENTRAL JAVA INDONESIA USING SPATIAL-POISSON REGRESSION." MEDIA STATISTIKA 11, no. 2 (December 30, 2018): 135–45. http://dx.doi.org/10.14710/medstat.11.2.135-145.

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Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression. Keywords: Maternal and Infant mortality, Poisson, GWPR

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Chakraborty, Sounak, Malay Ghosh, and Bani K. Mallick. "Bayesian nonlinear regression for large p small n problems." Journal of Multivariate Analysis 108 (July 2012): 28–40. http://dx.doi.org/10.1016/j.jmva.2012.01.015.

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