Relevant bibliographies by topics / Weighted regression estimator

Academic literature on the topic 'Weighted regression estimator'

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Published: 8 September 2023

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Journal articles on the topic "Weighted regression estimator"

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Kalina, Jan, and Jan Tichavský. "On Robust Estimation of Error Variance in (Highly) Robust Regression." Measurement Science Review 20, no. 1 (February 1, 2020): 6–14. http://dx.doi.org/10.2478/msr-2020-0002.

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AbstractThe linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes.
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Zhou, Xiaoshuang, Xiulian Gao, Yukun Zhang, Xiuling Yin, and Yanfeng Shen. "Efficient Estimation for the Derivative of Nonparametric Function by Optimally Combining Quantile Information." Symmetry 13, no. 12 (December 10, 2021): 2387. http://dx.doi.org/10.3390/sym13122387.

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In this article, we focus on the efficient estimators of the derivative of the nonparametric function in the nonparametric quantile regression model. We develop two ways of combining quantile regression information to derive the estimators. One is the weighted composite quantile regression estimator based on the quantile weighted loss function; the other is the weighted quantile average estimator based on the weighted average of quantile regression estimators at a single quantile. Furthermore, by minimizing the asymptotic variance, the optimal weight vector is computed, and consequently, the optimal estimator is obtained. Furthermore, we conduct some simulations to evaluate the performance of our proposed estimators under different symmetric error distributions. Simulation studies further illustrate that both estimators work better than the local linear least square estimator for all the symmetric errors considered except the normal error, and the weighted quantile average estimator performs better than the weighted composite quantile regression estimator in most situations.
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Cai, Zongwu. "REGRESSION QUANTILES FOR TIME SERIES." Econometric Theory 18, no. 1 (February 2002): 169–92. http://dx.doi.org/10.1017/s0266466602181096.

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In this paper we study nonparametric estimation of regression quantiles for time series data by inverting a weighted Nadaraya–Watson (WNW) estimator of conditional distribution function, which was first used by Hall, Wolff, and Yao (1999, Journal of the American Statistical Association 94, 154–163). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution estimator for α-mixing time series at both boundary and interior points, and we show that the WNW conditional distribution estimator not only preserves the bias, variance, and, more important, automatic good boundary behavior properties of local linear “double-kernel” estimators introduced by Yu and Jones (1998, Journal of the American Statistical Association 93, 228–237), but also has the additional advantage of always being a distribution itself. Second, it is shown that under some regularity conditions, the WNW conditional quantile estimator is weakly consistent and normally distributed and that it inherits all good properties from the WNW conditional distribution estimator. A small simulation study is carried out to illustrate the performance of the estimates, and a real example is also used to demonstrate the methodology.
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Koenker, Roger, and Kevin F. Hallock. "Quantile Regression." Journal of Economic Perspectives 15, no. 4 (November 1, 2001): 143–56. http://dx.doi.org/10.1257/jep.15.4.143.

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Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight.
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Rahmawati, Dyah P., I. N. Budiantara, Dedy D. Prastyo, and Made A. D. Octavanny. "Mixed Spline Smoothing and Kernel Estimator in Biresponse Nonparametric Regression." International Journal of Mathematics and Mathematical Sciences 2021 (March 11, 2021): 1–14. http://dx.doi.org/10.1155/2021/6611084.

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Mixed estimators in nonparametric regression have been developed in models with one response. The biresponse cases with different patterns among predictor variables that tend to be mixed estimators are often encountered. Therefore, in this article, we propose a biresponse nonparametric regression model with mixed spline smoothing and kernel estimators. This mixed estimator is suitable for modeling biresponse data with several patterns (response vs. predictors) that tend to change at certain subintervals such as the spline smoothing pattern, and other patterns that tend to be random are commonly modeled using kernel regression. The mixed estimator is obtained through two-stage estimation, i.e., penalized weighted least square (PWLS) and weighted least square (WLS). Furthermore, the proposed biresponse modeling with mixed estimators is validated using simulation data. This estimator is also applied to the percentage of the poor population and human development index data. The results show that the proposed model can be appropriately implemented and gives satisfactory results.
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Zhang, Zhengyu. "LOCAL PARTITIONED QUANTILE REGRESSION." Econometric Theory 33, no. 5 (September 19, 2016): 1081–120. http://dx.doi.org/10.1017/s0266466616000293.

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In this paper, we consider the nonparametric estimation of a broad class of quantile regression models, in which the partially linear, additive, and varying coefficient models are nested. We propose for the model a two-stage kernel-weighted least squares estimator by generalizing the idea of local partitioned mean regression (Christopeit and Hoderlein, 2006, Econometrica 74, 787–817) to a quantile regression framework. The proposed estimator is shown to have desirable asymptotic properties under standard regularity conditions. The new estimator has three advantages relative to existing methods. First, it is structurally simple and widely applicable to the general model as well as its submodels. Second, both the functional coefficients and their derivatives up to any given order can be estimated. Third, the procedure readily extends to censored data, including fixed or random censoring. A Monte Carlo experiment indicates that the proposed estimator performs well in finite samples. An empirical application is also provided.
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Schreuder, H. T., and Z. Ouyang. "Optimal sampling strategies for weighted linear regression estimation." Canadian Journal of Forest Research 22, no. 2 (February 1, 1992): 239–47. http://dx.doi.org/10.1139/x92-031.

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Our strong effort to find an optimal sampling strategy that was clearly superior to other strategies for a range of linearity conditions and variance structures for linear models showed that several sampling strategies turned out to be equally efficient. Each of these stratified the population to the maximum extent feasible, i.e., used n strata based on a covariate. Which of two ways of stratification to use and how units in each stratum were selected (simple random sampling or sampling with probability proportional to size) did not seem to matter much. Two regression estimators, one considering both probability and variance weights (Ŷgr) and one considering only probability weights (Ŷpi), are preferred estimators with the five efficient sampling selection schemes that select one unit per stratum with either equal or unequal probability sampling. The bootstrap variance estimator is generally the least biased, yet conservative, variance estimator and yields reliable coverage rates with 95% confidence intervals for most populations studied.
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Tao, Li, Lingnan Tai, Manling Qian, and Maozai Tian. "A New Instrumental-Type Estimator for Quantile Regression Models." Mathematics 11, no. 15 (August 4, 2023): 3412. http://dx.doi.org/10.3390/math11153412.

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This paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The estimator is built upon the minimum distance, which is defined as the weighted average of the conventional individual instrumental variable quantile regression slope estimators. The weights assigned to each estimator are determined by the inverses of their corresponding individual variance–covariance matrices. The implementation of the estimation has many advantages in terms of computational efforts and simplifies the asymptotic distribution. Furthermore, the paper shows consistency and asymptotic normality for sequential and simultaneous asymptotics. Additionally, it presents an empirical application that investigates the income elasticity of health expenditures.
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Glynn, Adam N., and Kevin M. Quinn. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis 18, no. 1 (2010): 36–56. http://dx.doi.org/10.1093/pan/mpp036.

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In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called “double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.
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Laome, Lilis, I. Nyoman Budiantara, and Vita Ratnasari. "Estimation Curve of Mixed Spline Truncated and Fourier Series Estimator for Geographically Weighted Nonparametric Regression." Mathematics 11, no. 1 (December 28, 2022): 152. http://dx.doi.org/10.3390/math11010152.

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Geographically Weighted Regression (GWR) is the development of multiple linear regression models used in spatial data. The assumption of spatial heterogeneity results in each location having different characteristics and allows the relationships between the response variable and each predictor variable to be unknown, hence nonparametric regression becomes one of the alternatives that can be used. In addition, regression functions are not always the same between predictor variables. This study aims to use the Geographically Weighted Nonparametric Regression (GWNR) model with a mixed estimator of truncated spline and Fourier series. Both estimators are expected to overcome unknown data patterns in spatial data. The mixed GWNR model estimator is then determined using the Weighted Maximum Likelihood Estimator (WMLE) technique. The estimator’s characteristics are then determined. The results of the study found that the estimator of the mixed GWNR model is an estimator that is not biased and linear to the response variable y.
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Dissertations / Theses on the topic "Weighted regression estimator"

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Liu, Yang. "Analysis of Dependently Truncated Sample Using Inverse Probability Weighted Estimator." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/110.

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Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.
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Zhang, Zongjun. "Adaptive Robust Regression Approaches in data analysis and their Applications." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445343114.

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Yang, Yani. "Dimension reduction in the regressions through weighted variance estimation." HKBU Institutional Repository, 2009. http://repository.hkbu.edu.hk/etd_ra/1073.

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Edlund, Per-Olov. "Preliminary estimation of transfer function weights : a two-step regression approach." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1989. http://www.hhs.se/efi/summary/291.htm.

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LeSage, James P., and Manfred M. Fischer. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation." WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/3968/1/SSRN%2Did980965.pdf.

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This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feed- back nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates. (authors' abstract)
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Gaspard, Guetchine. "FLOOD LOSS ESTIMATE MODEL: RECASTING FLOOD DISASTER ASSESSMENT AND MITIGATION FOR HAITI, THE CASE OF GONAIVES." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1236.

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This study aims at developing a model to estimate flood damage cost caused in Gonaives, Haiti by Hurricane Jeanne in 2004. In order to reach this goal, the influence of income, inundation duration and inundation depth, slope, population density and distance to major roads on the loss costs was investigated. Surveyed data were analyzed using Excel and ArcGIS 10 software. The ordinary least square and the geographically weighted regression analyses were used to predict flood damage costs. Then, the estimates were delineated using voronoi geostatistical map tool. As a result, the factors account for the costs as high as 83%. The flood damage cost in a household varies between 24,315 through 37,693 Haitian Gourdes (approximately 607.875 through 942.325 U.S. Dollars). Severe damages were spotted in the urban area and in the rural section of Bassin whereas very low and low losses are essentially found in Labranle. The urban area was more severely affected by comparison with the rural area. Damages in the urban area are estimated at 41,206,869.57USD against 698,222,174.10 17,455,554.35USD in the rural area. In the urban part, damages were more severe in Raboteau-Jubilée and in Downtown but Bigot-Parc Vincent had the highest overall damage cost estimated at 9,729,368.95 USD. The lowest cost 7,602,040.42USD was recorded in Raboteau. Approximately, 39.38% of the rural area underwent very low to moderate damages. Bassin was the most severely struck by the 2004 floods, but Bayonnais turned out to have the highest loss cost: 4,988,487.66 USD. Bassin along with Labranle had the least damage cost, 2,956,131.11 and 2,268,321.41 USD respectively. Based on the findings, we recommended the implementation and diversification of income-generating activities, the maintenance and improvement of drains, sewers and gullies cleaning and the establishment of conservation practices upstream of the watersheds. In addition, the model should be applied and validated using actual official records as reference data. Finally, the use of a calculation-based approach is suggested to determine flood damage costs in order to reduce subjectivity during surveys.
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Zhuofan, Wu. "Proposta de um modelo de regressão binária com resposta contínua aplicado à análise dos dados do SINASC: identificação de fatores de risco para o baixo peso ao nascer." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/17/17139/tde-25032011-122803/.

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O presente estudo tem por objetivo estudar a aplicabilidade de modelos de regressão binária com resposta contínua na análise de dados do SINASC (Sistema de Informações de Nascidos Vivos), analisando suas vantagens, limitações e estratégias na estimação de parâmetros ao identi…car os fatores de riscos para baixo peso ao nascer. Muitos autores vêm utilizando os dados do SINASC para estudar as variáveis que estão associadas ao baixo peso ao nascer. Estes autores geralmente utilizam o modelo usual de regressão logística, o qual analisa somente respostas binárias (a variável resposta é codi…cada como 1: baixo peso ao nascer, 0: caso contrário). O modelo de regressão com resposta contínua foi utilizado para estudar as variáveis associadas aos recém-nascidos com maior propensão a um peso ao nascer inferior ao ponto de corte 2500g, ou seja, a resposta é expressa em uma variável contínua. Nesta situação, uma extensão do modelo tradicional foi utilizada visando a possibilidade de obter-se estimativas mais precisas. Para a estimação de parâmetros do modelo de regressão binária com resposta contínua, foi utilizado o método da máxima verossimilhança. Os resultados obtidos a partir da metodologia proposta possui as seguintes vantagens em relação ao modelo usual: (a) o modelo de regressão proposto foi capaz de predizer o baixo peso ao nascer com maior precisão; (b) o modelo proposto evita problemas de separação persistentes em modelos usuais. Desta forma, o modelo estudado poderá oferecer signi…cativas contribuições à Saúde Coletiva, ao trazer uma nova possibilidade de análise de dados desta área.
The objective of this dissertation is to study the applicability of binary regression models for continuous outcomes in the data analysis from SINASC (Brazilian Live Births Information System), analyzing its advantages, limitations and strategies in the estimation of parameters, when identifying the risk factors for low-birth-weight. Many authors have been using data from SINASC to study the variables that are associated with the low-birth-weight. These authors typically use the usual logistic regression model, which analyzes only binary responses (the dependent variable is coded as 1 for low-birth-weight and 0 for otherwise). The regression model with continuous response was proposed and used to study the variables associated with the newborns with higher propensity to a birth weight below the cutoff point of 2500 g, that is, the answer is expressed as a continuous variable. In this situation, an extension method of the traditional model was used in order to enable obtaining more accurate estimates. For the estimation of the parameters from binary regression model with continuous response, the maximum likelihood method was used. The results obtained from the proposed methodology brought these following advantages comparing with the usual model: (A) the proposed regression model was capable for predicting low birth weight with a bettter precision; (B) the proposed model can process the persistent problems of separation present in the conventional models. Thus, the studied method may offer significant contributions to the Public Health, bringing new possibilities for data analysis in this area.
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Can, Mutan Oya. "Comparison Of Regression Techniques Via Monte Carlo Simulation." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.

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The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares, least trimmed squares (LTS), Theil and weighted Theil are compared via computer simulation. In order to evaluate the estimator performance, mean, variance, bias, mean square error (MSE) and relative mean square error (RMSE) are computed.
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Boruvka, Audrey. "Data-driven estimation for Aalen's additive risk model." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/489.

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"Weighted quantile regression and oracle model selection." Thesis, 2009. http://library.cuhk.edu.hk/record=b6074984.

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In this dissertation I suggest a new (regularized) weighted quantile regression estimation approach for nonlinear regression models and double threshold ARCH (DTARCH) models. I allow the number of parameters in the nonlinear regression models to be fixed or diverge. The proposed estimation method is robust and efficient and is applicable to other models. I use the adaptive-LASSO and SCAD regularization to select parameters in the nonlinear regression models. I simultaneously estimate the AR and ARCH parameters in the DTARCH model using the proposed weighted quantile regression. The values of the proposed methodology are revealed.
Keywords: Weighted quantile regression, Adaptive-LASSO, High dimensionality, Model selection, Oracle property, SCAD, DTARCH models.
Under regularity conditions, I establish asymptotic distributions of the proposed estimators, which show that the model selection methods perform as well as if the correct submodels are known in advance. I also suggest an algorithm for fast implementation of the proposed methodology. Simulations are conducted to compare different estimators, and a real example is used to illustrate their performance.
Jiang, Xuejun.
Adviser: Xinyuan Song.
Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2009.
Includes bibliographical references (leaves 86-92).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
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Books on the topic "Weighted regression estimator"

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Toutenburg, Helge. MSE-comparisons between restricted least squares, mixed, and weighted mixed estimators with special emphasize [i.e. emphasis] to nested restrictions. Berlin: Akademie der Wissenschaften der DDR, Karl-Weierstrass-Institut für Mathematik, 1988.

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Book chapters on the topic "Weighted regression estimator"

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Mašíček, L. "Consistency of the Least Weighted Squares Regression Estimator." In Theory and Applications of Recent Robust Methods, 183–94. Basel: Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_17.

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Crossa, José, J. Jesús Cerón-Rojas, Johannes W. R. Martini, Giovanny Covarrubias-Pazaran, Gregorio Alvarado, Fernando H. Toledo, and Velu Govindan. "Theory and Practice of Phenotypic and Genomic Selection Indices." In Wheat Improvement, 593–616. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_32.

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AbstractThe plant net genetic merit is a linear combination of trait breeding values weighted by its respective economic weights whereas a linear selection index (LSI) is a linear combination of phenotypic or genomic estimated breeding values (GEBV) which is used to predict the net genetic merit of candidates for selection. Because economic values are difficult to assign, some authors developed economic weight-free LSI. The economic weights LSI are associated with linear regression theory, while the economic weight-free LSI is associated with canonical correlation theory. Both LSI can be unconstrained or constrained. Constrained LSI imposes restrictions on the expected genetic gain per trait to make some traits change their mean values based on a predetermined level, while the rest of the traits change their values without restriction. This work is geared towards plant breeders and researchers interested in LSI theory and practice in the context of wheat breeding. We provide the phenotypic and genomic unconstrained and constrained LSI, which together cover the theoretical and practical cornerstone of the single-stage LSI theory in plant breeding. Our main goal is to offer researchers a starting point for understanding the core tenets of LSI theory in plant selection.
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Heumann, Christian, and Shalabh. "Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions." In Recent Advances in Linear Models and Related Areas, 401–16. Heidelberg: Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2064-5_22.

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Lu, Dejun, Weifeng Zhang, Kaixuan Cuan, and Pengfei Liu. "Reflectance Estimation Based on Locally Weighted Linear Regression Methods." In Communications in Computer and Information Science, 93–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1648-7_8.

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Koul, Hira L., and Pei Geng. "Weighted Empirical Minimum Distance Estimators in Berkson Measurement Error Regression Models." In Analytical Methods in Statistics, 31–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48814-7_3.

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Riego del Castillo, Virginia, Lidia Sánchez-González, Laura Fernández-Robles, Manuel Castejón-Limas, and Rubén Rebollar. "Estimation of Lamb Weight Using Transfer Learning and Regression." In Lecture Notes in Networks and Systems, 23–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18050-7_3.

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Molinero-Parejo, Ramón. "Geographically Weighted Methods to Validate Land Use Cover Maps." In Land Use Cover Datasets and Validation Tools, 255–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_13.

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AbstractOne of the most commonly used techniques for validating Land Use Cover (LUC) maps are the accuracy assessment statistics derived from the cross-tabulation matrix. However, although these accuracy metrics are applied to spatial data, this does not mean that they produce spatial results. The overall, user’s and producer’s accuracy metrics provide global information for the entire area analysed, but shed no light on possible variations in accuracy at different points within this area, a shortcoming that has been widely criticized. To address this issue, a series of techniques have been developed to integrate a spatial component into these accuracy assessment statistics for the analysis and validation of LUC maps. Geographically Weighted Regression (GWR) is a local technique for estimating the relationship between a dependent variable with respect to one or more independent variables or explanatory factors. However, unlike traditional regression techniques, it considers the distance between data points when estimating the coefficients of the regression points using a moving window. Hence, it assumes that geographic data are non-stationary i.e., they vary over space. Geographically weighted methods provide a non-stationary analysis, which can reveal the spatial relationships between reference data obtained from a LUC map and classified data. Specifically, logistic GWR is used in this chapter to estimate the accuracy of each LUC data point, so allowing us to observe the spatial variation in overall, user’s and producer’s accuracies. A specific tool (Local accuracy assessment statistics) was specially developed for this practical exercise, aimed at validating a Land Use Cover map. The Marqués de Comillas region was selected as the study area for implementing this tool and demonstrating its applicability. For the calculation of the user’s and producer’s accuracy metrics, we selected the tropical rain forest category [50] as an example. Furthermore, a series of maps were obtained by interpolating the results of the tool, so enabling a visual interpretation and a description of the spatial distribution of error and accuracy.
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Abdelhady, Aya Salama, Aboul Ella Hassanien, Yasser Mahmoud Awad, Moataz El-Gayar, and Aly Fahmy. "Automatic Sheep Weight Estimation Based on K-Means Clustering and Multiple Linear Regression." In Advances in Intelligent Systems and Computing, 546–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_50.

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Tassinari, Giorgio, and Demetrio Panarello. "The effectiveness of marketing tools in a consumer goods market in Italy during the Great Recession (2010-2015)." In Proceedings e report, 105–10. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.20.

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In the case of markets characterized by a stationary primary demand, the relevant dimension for measuring a company’s success is represented by market shares. The paper aims to build and comment on a model that gauges the competitive effects of marketing maneuvers on market shares, with reference to tea-based beverages in Italy in the period November 2010 – October 2015. This analysis will be instrumental in establishing the effectiveness of marketing policies based on promotions or advertising. We estimate such a model on weekly data provided by IRI Infoscan and Nielsen, involving the top five brands in the Italian market. After a descriptive analysis and a stationarity test, we estimate a Multinomial Logit model, making use of the Seemingly Unrelated Regressions method. The results allow us to identify the effectiveness of each brand’s marketing policies. Moreover, they enable us to derive the matrices of direct and cross elasticities of brands’ market shares with respect to the main marketing tools (price, promotions, distribution, advertising investments) and to compare basic and average market shares. Based on these results, it is therefore possible to identify the market’s competitive structure, revealing the most incisive factors to be price and weighted distribution, while advertising investments are significant in only a few cases and elasticities are remarkably low. The competitive structure appears to be of a horizontal type (i.e., cross elasticities do not vary greatly).
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Cho, Wanhyun, Junki Kim, Myung-Hwan Na, Sangkyoon Kim, and Hyejin Lee. "Estimation of Weights in Growth Stages of Onions Using Statistical Regression Models and Deep Learning Algorithm." In Advances in Computer Science and Ubiquitous Computing, 159–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9343-7_22.

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Conference papers on the topic "Weighted regression estimator"

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Zuo, Weibing. "A New Stochastic Restricted Liu Estimator in Weighted Mixed Regression." In 2009 Second International Symposium on Computational Intelligence and Design. IEEE, 2009. http://dx.doi.org/10.1109/iscid.2009.68.

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Pati, Kafi Dano, Robiah Adnan, Bello Abdulkadir Rasheed, and Muhammad Alias MD. J. "Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954633.

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Cahyono, Endro Setyo, and Muhammad Alifian Nuriman. "Auxiliary information based exponentially weighted moving coefficient of variation control chart using regression estimator (AIB-EWMCVReg)." In TOWARD ADAPTIVE RESEARCH AND TECHNOLOGY DEVELOPMENT FOR FUTURE LIFE. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0114193.

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Kuzairi, N. Chamidah, and I. N. Budiantara. "Theoretical Study of Fourier Series Estimator in Semiparametric Regression for Longitudinal Data Based on Weighted Least Square Optimization." In 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200303.064.

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Broadie, Mark, Yiping Du, and Ciamac C. Moallemi. "Risk estimation via weighted regression." In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6148077.

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Vegh, J., and Andrew Milligan. "Revisiting Legacy Weight Relationships Using Machine Learning Techniques." In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17494.

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This paper investigates the application of K-Means Clustering algorithms to traditional aircraft conceptual-level weight estimation techniques. As a proof of concept demonstration, application was narrowed to fuselage basic weight estimation with expansion to additional component weights as a planned follow on activity. A variety of weight sources were parsed and curated to produce a large, diverse dataset consisting of 82 separate aircraft with a corresponding new universal baseline regression to compare against. A K-Means Clustering algorithm was then employed that sorted aircraft into groupings based on configuration as well as topology and created an associated regression for each grouping. Configuration-based groupings utilized information such as a high-level abstraction of the structural layout as well as whether the aircraft is a fixed-wing or rotary-wing vehicle. Topology-cased groupings utilized information such as landing gear location and possession of a cargo ramp or wing. The configuration-based groupings showed modest improvement compared to the baseline regression while the topology-based groupings consistently outperformed both the baseline regression as well as the configuration-based regressions. Under all conditions, a subset of the data associated with fixed-wing aircraft was shown to be an outlier in regards to error as a result of a large range of weight and speed scales, as well as possible secondary pressurization impacts. Special treatment of the winged dataset led to further reduction in error based on unique design features, presenting an overall fuselage weight estimation methodology that leverages machine learning algorithms that can improve and inform existing best practices.
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Juan Luo. "Parameter estimation in geographically weighted regression." In 2009 17th International Conference on Geoinformatics. IEEE, 2009. http://dx.doi.org/10.1109/geoinformatics.2009.5292988.

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Roh, Myung-Il, Seong-Ho Seo, Hyun-Kyoung Shin, Nam-Kug Ku, Sol Ha, and Ki-Su Kim. "Simplified Model for the Weight Estimation of Floating Offshore Plant Using the Statistical Method." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24379.

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The weight information of a floating offshore plant, such as an FPSO, is one of the important data to estimate the amount of production material and to determine the production method for its construction. In addition, the weight information is a key factor which affects in the building cost and production period of the offshore plant. Although the importance of the weight has long been recognized, the weight has been roughly estimated by using the existing design and production data, and designer’s experience. To improve this task, a simplified model for the weight estimation of the offshore plant using the statistical method was developed in this study. To do this, various past records to estimate the weight of the offshore plant were collected through the literature survey, and then the correlation analysis and the multiple regression analysis were performed to develop the simplified model for the weight estimation. Finally, to evaluate the applicability of the developed model, it was applied to some examples of the weight estimation of topsides of the offshore plant. The results showed that the developed model can be applied the weight estimation process of the offshore plant at the early design stage.
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Paredes, Jose L., and Gonzalo R. Arce. "Compressive sensing signal reconstruction by weighted median regression estimates." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495738.

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Lin, Zhipeng. "ML Estimation of Spatial Panel Data Geographically Weighted Regression Model." In 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5999234.

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Reports on the topic "Weighted regression estimator"

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Villamizar-Villegas, Mauricio, and Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, November 2020. http://dx.doi.org/10.32468/be.1141.

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The literature that employs Regression Discontinuity Designs (RDD) typically stacks data across time periods and cutoff values. While practical, this procedure omits useful time heterogeneity. In this paper we decompose the RDD treatment effect into its weighted time-value parts. This analysis adds richness to the RDD estimand, where each time-specific component can be different and informative in a manner that is not expressed by the single cutoff or pooled regressions. To illustrate our methodology, we present two empirical examples: one using repeated cross-sectional data and another using time-series. Overall, we show a significant heterogeneity in both cutoff and time-specific effects. From a policy standpoint, this heterogeneity can pick up key differences in treatment across economically relevant episodes. Finally, we propose a new estimator that uses all observations from the original design and which captures the incremental effect of policy given a state variable. We show that this estimator is generally more precise compared to those that exclude observations exposed to other cutoffs or time periods. Our proposed framework is simple and easily replicable and can be applied to any RDD application that carries an explicitly traceable time dimension.
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Kott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, April 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0047.2203.

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When fitting observations from a complex survey, the standard regression model assumes that the expected value of the difference between the dependent variable and its model-based prediction is zero, regardless of the values of the explanatory variables. A rarely failing extended regression model assumes only that the model error is uncorrelated with the model’s explanatory variables. When the standard model holds, it is possible to create alternative analysis weights that retain the consistency of the model-parameter estimates while increasing their efficiency by scaling the inverse-probability weights by an appropriately chosen function of the explanatory variables. When a regression model is used to impute for missing item values in a complex survey and when item missingness is a function of the explanatory variables of the regression model and not the item value itself, near unbiasedness of an estimated item mean requires that either the standard regression model for the item in the population holds or the analysis weights incorporate a correctly specified and consistently estimated probability of item response. By estimating the parameters of the probability of item response with a calibration equation, one can sometimes account for item missingness that is (partially) a function of the item value itself.
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Giltinan, D. M., R. J. Carroll, and D. Ruppert. Some New Estimation Methods for Weighted Regression When There are Possible Outliers. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada152104.

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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25 miles and 0.5 miles) at each selected road segment, with the number of crashes on each road segment used as the dependent variable. The generalized linear models with negative binomial distribution (GLM-based NB model) as well as the geographically weighted negative binomial regression (GWNBR) and geographically weighted negative binomial regression model with global dispersion (GWNBRg) were developed and compared. This research relied on data for 147 geographically distributed road segments for modeling and data for 49 segments for validation. The annual average daily traffic (AADT), light commercial land use, light industrial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing the teen crash frequency. Both methods have good predictive capabilities and can be used to estimate the teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity among road teen crashes and the associated risk factors.
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Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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Goetsch, Arthur L., Yoav Aharoni, Arieh Brosh, Ryszard (Richard) Puchala, Terry A. Gipson, Zalman Henkin, Eugene D. Ungar, and Amit Dolev. Energy Expenditure for Activity in Free Ranging Ruminants: A Nutritional Frontier. United States Department of Agriculture, June 2009. http://dx.doi.org/10.32747/2009.7696529.bard.

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Heat production (HP) or energy expenditure for activity (EEa) is of fundamental nutritional importance for livestock because it determines the proportion of ingested nutrients available for productive functions. Previous estimates of EEa are unreliable and vary widely with different indirect methodologies. This leads to erroneous nutritional strategies, especially when intake on pasture does not meet nutritional requirements and supplementation is necessary for acceptable production. Therefore, the objective of this project was to measure EEa in different classes of livestock (beef cattle and goats) over a wide range of ecological and management conditions to develop and evaluate simple means of prediction. In the first study in Israel, small frame (SF) and large frame (LF) cows (268 and 581 kg) were monitored during spring, summer, and autumn. Feed intake by SF cows per unit of metabolic weight was greater (P < 0.001) than that by LF cows in both spring and summer and their apparent selection of higher quality herbage in spring was greater (P < 0.10) than that of LF cows. SF cows grazed more hours per day and walked longer distances than the LF cows during all seasons. The coefficient of specific costs of activities (kJ•kg BW-0.75•d-1) and of locomotion (J•kg BW-0.75•m-1) were smaller for the SF cows. In the second study, cows were monitored in March, May, and September when they grazed relatively large plots, 135 and 78 ha. Energy cost coefficients of standing, grazing, and horizontal locomotion derived were similar to those of the previous study based on data from smaller plots. However, the energy costs of walking idle and of vertical locomotion were greater than those found by Brosh et al. (2006) but similar to those found by Aharoni et al. (2009). In the third study, cows were monitored in February and May in a 78-ha plot with an average slope of 15.5°, whereas average plot slopes of the former studies ranged between 4.3 and 6.9°. Energy cost coefficients of standing, grazing, and walking idle were greater than those calculated in the previous studies. However, the estimated energy costs of locomotion were lower in the steeper plot. A comparison on a similar HP basis, i.e., similar metabolizable energy (ME) intake, shows that the daily energy spent on activities in relation to daily HP increased by 27% as the average plot slope increased from 5.8 and 6.02 to 15.5°. In the fourth study, cows grazing in a woodland habitat were monitored as in previous studies in December, March, and July. Data analysis is in progress. In the first US experiment, Boer and Spanish does with two kids were used in an experiment beginning in late spring at an average of 24 days after kidding. Two does of each breed resided in eight 0.5-ha grass/forb pastures. Periods of 56, 60, 63, 64, and 73 days in length corresponded to mid-lactation, early post-weaning, the late dry period, early gestation, and mid-gestation. EEa expressed as a percentage of the ME requirement for maintenance plus activity in confinement (EEa%) was not influenced by stocking rate, breed, or period, averaging 49%. Behavioral activities (e.g., time spent grazing, walking, and idle, distance traveled) were not highly related to EEa%, although no-intercept regressions against time spent grazing/eating and grazing/eating plus walking indicated an increase in EEa% of 5.8 and 5.1%/h, respectively. In the second study, animal types were yearling Angora doeling goats, yearling Boer wether goats, yearling Spanish wether goats, and Rambouilletwether sheep slightly more than 2 yr of age. Two animals of each type were randomly allocated to one of four pastures 9.3, 12.3, 4.6, and 1.2 ha in area. The experiment was conducted in the summer with three periods, 30, 26, and 26 days in length. EEa% was affected by an interaction between animal type and period (Angora: 16, 17, and 15; Boer: 60, 67, and 34; Spanish: 46, 62, and 42; sheep: 22, 12, and 22% in periods 1, 2, and 3, respectively (SE = 6.1)). EEa% of goats was predicted with moderate accuracy (R2 = 0.40-0.41) and without bias from estimates of 5.8 and 5.1%/h spent grazing/eating and grazing/eating plus walking, respectively, determined in the first experiment; however, these methods were not suitable for sheep. These methods of prediction are simpler and more accurate than currently recommended for goats by the National Research Council.
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