Relevant bibliographies by topics / Weighted regression estimator

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Weighted regression estimator'

Author: Grafiati
Published: 8 September 2023
Last updated: 31 July 2025

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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 (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 i
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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 (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 o
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Cai, Zongwu. "REGRESSION QUANTILES FOR TIME SERIES." Econometric Theory 18, no. 1 (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, v
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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 commonl
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Koenker, Roger, and Kevin F. Hallock. "Quantile Regression." Journal of Economic Perspectives 15, no. 4 (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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Zhang, Zhengyu. "LOCAL PARTITIONED QUANTILE REGRESSION." Econometric Theory 33, no. 5 (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
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Zheng, Cheng, Sayan Dasgupta, Yuxiang Xie, Asad Haris, and Ying-Qing Chen. "On Data-Enriched Logistic Regression." Mathematics 13, no. 3 (2025): 441. https://doi.org/10.3390/math13030441.

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Biomedical researchers typically investigate the effects of specific exposures on disease risks within a well-defined population. The gold standard for such studies is to design a trial with an appropriately sampled cohort. However, due to the high cost of such trials, the collected sample sizes are often limited, making it difficult to accurately estimate the effects of certain exposures. In this paper, we discuss how to leverage the information from external ”big data” (datasets with significantly larger sample sizes) to improve the estimation accuracy at the risk of introducing a small amou
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Schreuder, H. T., H. G. Li, and J. W. Hazard. "PPS and Random Sampling Estimation Using some Regression and Ratio Estimators for Underlying Linear and Curvilinear Models." Forest Science 33, no. 4 (1987): 997–1009. http://dx.doi.org/10.1093/forestscience/33.4.997.

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Abstract Two thousand samples of 30 units were drawn from selected populations for which linear or curvilinear underlying models were postulated between the variable of interest and a covariate. Ratio, and linear and nonlinear regression estimators were compared for bias and relative efficiency of the estimates generated. Regression estimators were found to be the most precise estimators of totals for both random and probability proportional to size (PPS) sampling for a series of tree populations for samples of size 30. The weighted regression estimator in PPS sampling was consistently more ef
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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 (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 c
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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 th
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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 i
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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 tradition
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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
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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 co
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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 wei
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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 t
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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. 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. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_17.

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Al Hayani, Mahmoud H. Eiada, and Mustafa I. Alheety. "Using Corrected Biased for Developing New Weighted Mixed Estimator for Linear Regression Model." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70924-1_39.

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Crossa, José, J. Jesús Cerón-Rojas, Johannes W. R. Martini, et al. "Theory and Practice of Phenotypic and Genomic Selection Indices." In Wheat Improvement. 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 uncon
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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. 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. 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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48814-7_3.

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Alheety, Mustafa I., Muhammad Qasim, Kristofer Månsson, and B. M. Golam Kibria. "On Some Weighted Mixed Ridge Regression Estimators: Theory, Simulation and Application." In Springer Proceedings in Mathematics & Statistics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4876-1_6.

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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. 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. 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 de
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Penet, Maxime, and Gaetan Le Gall. "Robust Inverse Vehicle Map Regression Based on Laplace Distribution." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_113.

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AbstractThis paper deals with the identification of the relationship between vehicle acceleration and driver available actuators. The vehicle is modeled based on how a driving task is performed. The model is constructed using neural networks whose weights are identified using data collected through non tailored driving sessions. To take into account disturbances, the model follows a Laplace distribution. This leads to a more robust estimate of the vehicle knowledge and the confidence we have in it. The approach is illustrated on a prototype vehicle equipped with a petrol engine, plus a device
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Conference papers on the topic "Weighted regression estimator"

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Duan, Xinrong, Xiao Niu, and Yan Xun. "Remote sensing estimation of soil organic matter based on multiscale geographical weighted regression." In 2024 International Conference on Remote Sensing and Digital Earth, edited by Jie Cheng, Kegen Yu, and Mahmoud Reza Delavar. SPIE, 2025. https://doi.org/10.1117/12.3059079.

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Halder, Anubhav, Gaurav Makkar, and Farhan Gandhi. "Gross Weight, CG Position, and Rotor Flapping Prediction for a Compound Helicopter using Machine Learning." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1328.

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This study investigates the use of machine learning (ML) models to estimate the gross weight (GW), the longitudinal position of the center of gravity (CGx), and 1/rev cyclic flapping angles (β1c and β1s) of a compound helicopter with three redundant controls - main rotor RPM, collective propeller thrust, and stabilator angle. Neural Network (NN), Gaussian Process for Regression (GPR), and Support Vector Machine (SVM) algorithms are employed to develop estimation models using supervised training. The airspeed, redundant controls, main rotor controls, aircraft attitudes, and main rotor torque ar
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Halder, Anubhav, Jonah Whitt, Farhan Gandhi, and Etana Ferede. "Gross Weight, CG Position, and Airspeed Estimation of Large Multicopters for Advanced Air Mobility." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-300.

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This paper presents a comprehensive evaluation of machine learning approaches for real-time operational/ flight parameter estimation in large electric vertical takeoff and landing (eVTOL) vehicles, addressing the challenges of time-varying payloads and atmospheric disturbances in Advanced Air Mobility (AAM) missions. Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM), are compared for their ability to estimate gross weight (GW), longitudinal center of gravity position (CGx), and airspeed (Ux) using readily available flight control inputs and
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Al-Alawi, Ali Humaid, Nazih Aloui, Thaer Abou Hamza, Mohamed Chams Elhoda, Aziz hmoud Echabibi, and Nour Mohamed Sobhy. "Tilapia Fish Weight Estimation in Freshwater Using Deep Learning and Regression Methods." In 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E). IEEE, 2025. https://doi.org/10.1109/ai2e64943.2025.10983247.

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Hewe, Thomas Raphael A., Karl Monico J. Mondia, and Paul Emmanuel G. Empas. "Advancing Poultry Farming Efficiency Through YOLOv5 and Image Regression-Based Broiler Weight Estimation." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10827717.

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Carney, Alison, and Robert W. Hendricks. "The Viscosity of Dilute H2O-B(OH)3-LiOH Solutions." In CORROSION 2017. NACE International, 2017. https://doi.org/10.5006/c2017-09413.

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Abstract The viscosities of dilute solutions of H2O-B(OH)3-LiOH with compositions typical of the coolant of light water power reactors have been measured for three boron compositions between 1500 ppm (by weight) and 3500 ppm and three lithium concentrations between 10 ppm (by weight) and 40 ppm over a temperature range from 30°C to 60°C using a calibrated Cannon-Ubbelohde viscometer. The viscosities of these dilute solutions were shown to follow an Arrhenius equation of the form vc=AeB/T(1+k1cb+k2ca)where υc is the kinematic viscosity (m2/s) of a solution of composition cb ppm Li of the strong
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AL_Hayani, Mahmoud H. Eiada, and Mustafa I. Alheety. "Modified stochastic weighted mixed estimator for linear regression model." In 2ND INTERNATIONAL CONFERENCE OF MATHEMATICS, APPLIED SCIENCES, INFORMATION AND COMMUNICATION TECHNOLOGY. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0164247.

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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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Laome, Lilis, I. Nyoman Budiantara, and Vita Ratnasari. "Poverty modelling with spline truncated, Fourier series, and mixed estimator geographically weighted nonparametric regression." In INTERNATIONAL CONFERENCE ON ENGINEERING AND COMPUTER SCIENCE (ICECS) 2022: The Use of Innovative Technology in Accelerating Problems Sustainable Development. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0206173.

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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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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, 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
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Kott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, 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-probabili
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de Dieu Niyigena, Jean, Innocent Ngaruye, Joseph Nzabanita, and Martin Singull. Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-02.

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Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then
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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. Defense Technical Information Center, 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, 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
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Over, Thomas, Riki Saito, Andrea Veilleux, et al. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, 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 i
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Medina, Carlos, Jairo Núñez, and Jorge Andrés Tamayo. The Unemployment Subsidy Program in Colombia: An Assessment. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0011497.

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This paper assesses the effects of the Colombian Unemployment Subsidy (US), which includes benefits as well as training for some recipients. Using regression discontinuity and matching differences-in-differences estimators, the study finds that participation in the labor market, earnings of beneficiaries, and household income do not increase, and for some populations decrease during the 18 months after leaving the US program. Enrollment in formal health insurance falls. Effects on male heads of household include reductions in their earnings, decreases in their labor participation, and increase
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Goetsch, Arthur L., Yoav Aharoni, Arieh Brosh, et al. Energy Expenditure for Activity in Free Ranging Ruminants: A Nutritional Frontier. United States Department of Agriculture, 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
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