Relevant bibliographies by topics / Ordinary least squares method

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

Academic literature on the topic 'Ordinary least squares method'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Ordinary least squares method"

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Machado, Vieira Filho, and de Oliveira. "Forensic Speaker Verification Using Ordinary Least Squares." Sensors 19, no. 20 (October 10, 2019): 4385. http://dx.doi.org/10.3390/s19204385.

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In Brazil, the recognition of speakers for forensic purposes still relies on a subjectivity-based decision-making process through a results analysis of untrustworthy techniques. Owing to the lack of a voice database, speaker verification is currently applied to samples specifically collected for confrontation. However, speaker comparative analysis via contested discourse requires the collection of an excessive amount of voice samples for a series of individuals. Further, the recognition system must inform who is the most compatible with the contested voice from pre-selected individuals. Accordingly, this paper proposes using a combination of linear predictive coding (LPC) and ordinary least squares (OLS) as a speaker verification tool for forensic analysis. The proposed recognition technique establishes confidence and similarity upon which to base forensic reports, indicating verification of the speaker of the contested discourse. Therefore, in this paper, an accurate, quick, alternative method to help verify the speaker is contributed. After running seven different tests, this study preliminarily achieved a hit rate of 100% considering a limited dataset (Brazilian Portuguese). Furthermore, the developed method extracts a larger number of formants, which are indispensable for statistical comparisons via OLS. The proposed framework is robust at certain levels of noise, for sentences with the suppression of word changes, and with different quality or even meaningful audio time differences.
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Watagoda, Lasanthi C. R. Pelawa. "A Sub-Model Theorem for Ordinary Least Squares." International Journal of Statistics and Probability 8, no. 1 (November 19, 2018): 40. http://dx.doi.org/10.5539/ijsp.v8n1p40.

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Variable selection or subset selection is an important step in the process of model fitting. There are many ways to select the best subset of variables including Forward selection, Backward elimination, etcetera. Ordinary least squares (OLS) is one of the most commonly used methods of fitting the final model. Final sub-model can perform poorly if the variable selection process failed to choose the right number of variables. This paper gives a new theorem and a mathematical proof to illustrate the reason for the poor performances, when using the least squares method after variable selection.
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Sanchez, Juan. "Estimating Detection Limits in Chromatography from Calibration Data: Ordinary Least Squares Regression vs. Weighted Least Squares." Separations 5, no. 4 (October 8, 2018): 49. http://dx.doi.org/10.3390/separations5040049.

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It is necessary to determine the limit of detection when validating any analytical method. For methods with a linear response, a simple and low labor-consuming procedure is to use the linear regression parameters obtained in the calibration to estimate the blank standard deviation from the residual standard deviation (sres), or the intercept standard deviation (sb0). In this study, multiple experimental calibrations are evaluated, applying both ordinary and weighted least squares. Moreover, the analyses of replicated blank matrices, spiked at 2–5 times the lowest calculated limit values with the two regression methods, are performed to obtain the standard deviation of the blank. The limits of detection obtained with ordinary least squares, weighted least squares, the signal-to-noise ratio, and replicate blank measurements are then compared. Ordinary least squares, which is the simplest and most commonly applied calibration regression methodology, always overestimate the values of the standard deviations at the lower levels of calibration ranges. As a result, the detection limits are up to one order of magnitude greater than those obtained with the other approaches studied, which all gave similar limits.
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de Souza, Scheilla V. C., and Roberto G. Junqueira. "A procedure to assess linearity by ordinary least squares method." Analytica Chimica Acta 552, no. 1-2 (November 2005): 25–35. http://dx.doi.org/10.1016/j.aca.2005.07.043.

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YUKUTAKE, Kiyoshi, and Atsushi YOSHIMOTO. "Analysis of Lumber Demand and Supply in Japan : Price Elasticities by the Ordinary Least Squares Method, Two Stage Least Squares Method and Three Stage Least Squares Method." Japanese Journal of Forest Planning 36, no. 2 (2002): 81–98. http://dx.doi.org/10.20659/jjfp.36.2_81.

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Yanuar, Ferra. "The Simulation Study to Test the Performance of Quantile Regression Method With Heteroscedastic Error Variance." CAUCHY 5, no. 1 (November 30, 2017): 36. http://dx.doi.org/10.18860/ca.v5i1.4209.

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<div><p class="Keywords">The purpose of this article was to describe the ability of the quantile regression method in overcoming the violation of classical assumptions. The classical assumptions that are violated in this study are variations of non-homogeneous error or heteroscedasticity. To achieve this goal, the simulated data generated with the design of certain data distribution. This study did a comparison between the models resulting from the use of the ordinary least squares and the quantile regression method to the same simulated data. Consistency of both methods was compared with conducting simulation studies as well. This study proved that the quantile regression method had standard error, confidence interval width and mean square error (MSE) value smaller than the ordinary least squares method. Thus it can be concluded that the quantile regression method is able to solve the problem of heteroscedasticity and produce better model than the ordinary least squares. In addition the ordinary least squares is not able to solve the problem of heteroscedasticity.</p></div>
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Weiss, Andrew A. "A Comparison of Ordinary Least Squares and Least Absolute Error Estimation." Econometric Theory 4, no. 3 (December 1988): 517–27. http://dx.doi.org/10.1017/s0266466600013438.

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In a linear-regression model with heteroscedastic errors, we consider two tests: a Hausman test comparing the ordinary least squares (OLS) and least absolute error (LAE) estimators and a test based on the signs of the errors from OLS. It turns out that these are related by the well-known equivalence between Hausman and the generalized method of moments tests. Particular cases, including homoscedasticity and asymmetry in the errors, are discussed.
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Wang, Song-Gui, Shein-Chung Chow, and Siu-Keung Tse. "On ordinary least-squares methods for sample surveys." Statistics & Probability Letters 20, no. 3 (June 1994): 173–82. http://dx.doi.org/10.1016/0167-7152(94)90039-6.

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Cunia, T., and R. D. Briggs. "Forcing additivity of biomass tables: use of the generalized least squares method." Canadian Journal of Forest Research 15, no. 1 (February 1, 1985): 23–28. http://dx.doi.org/10.1139/x85-006.

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The generalized least squares procedure is applied to sample tree data for which additive biomass tables are required. This procedure is proposed as an alternative to the ordinary weighted least squares in order to account for the fact that several biomass components are measured on the same sample trees. The biomass tables generated by the generalized and the ordinary least squares are very similar, the confidence intervals are sometimes wider, sometimes narrower, but the prediction intervals are always narrower for the generalized least squares method.
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Long, Rebecca G. "The Crux of the Method: Assumptions in Ordinary Least Squares and Logistic Regression." Psychological Reports 103, no. 2 (October 2008): 431–34. http://dx.doi.org/10.2466/pr0.103.2.431-434.

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Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.
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Dissertations / Theses on the topic "Ordinary least squares method"

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Krueger, Justin Michael. "Parameter Estimation Methods for Ordinary Differential Equation Models with Applications to Microbiology." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78674.

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The compositions of in-host microbial communities (microbiota) play a significant role in host health, and a better understanding of the microbiota's role in a host's transition from health to disease or vice versa could lead to novel medical treatments. One of the first steps toward this understanding is modeling interaction dynamics of the microbiota, which can be exceedingly challenging given the complexity of the dynamics and difficulties in collecting sufficient data. Methods such as principal differential analysis, dynamic flux estimation, and others have been developed to overcome these challenges for ordinary differential equation models. Despite their advantages, these methods are still vastly underutilized in mathematical biology, and one potential reason for this is their sophisticated implementation. While this work focuses on applying principal differential analysis to microbiota data, we also provide comprehensive details regarding the derivation and numerics of this method. For further validation of the method, we demonstrate the feasibility of principal differential analysis using simulation studies and then apply the method to intestinal and vaginal microbiota data. In working with these data, we capture experimentally confirmed dynamics while also revealing potential new insights into those dynamics. We also explore how we find the forward solution of the model differential equation in the context of principal differential analysis, which amounts to a least-squares finite element method. We provide alternative ideas for how to use the least-squares finite element method to find the forward solution and share the insights we gain from highlighting this piece of the larger parameter estimation problem.
Ph. D.
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Haddad, Khaled. "Design flood estimation for ungauged catchments in Victoria ordinary & generalised least squares methods compared /." View thesis, 2008. http://handle.uws.edu.au:8081/1959.7/30369.

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Thesis (M.Eng. (Hons.)) -- University of Western Sydney, 2008.
A thesis submitted towards the degree of Master of Engineering (Honours) in the University of Western Sydney, College of Health and Science, School of Engineering. Includes bibliographical references.
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Anderson, Cynthia 1962. "A Comparison of Five Robust Regression Methods with Ordinary Least Squares: Relative Efficiency, Bias and Test of the Null Hypothesis." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc5808/.

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A Monte Carlo simulation was used to generate data for a comparison of five robust regression estimation methods with ordinary least squares (OLS) under 36 different outlier data configurations. Two of the robust estimators, Least Absolute Value (LAV) estimation and MM estimation, are commercially available. Three authormodified variations on MM were also included (MM1, MM2, and MM3). Design parameters that were varied include sample size (n=60 and n=180), number of independent predictor variables (2, 3 and 6), outlier density (0%, 5% and 15%) and outlier location (2x,2y s, 8x8y s, 4x,8y s and 8x,4y s). Criteria on which the regression methods were measured are relative efficiency, bias and a test of the null hypothesis. Results indicated that MM2 was the best performing robust estimator on relative efficiency. The best performing estimator on bias was MM1. The best performing regression method on the test of the null hypothesis was MM2. Overall, the MM-type robust regression methods outperformed OLS and LAV on relative efficiency, bias, and the test of the null hypothesis.
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Haubeltova, Libuse. "Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics." Thesis, Högskolan Dalarna, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-29979.

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The main purpose of this degree project is to reveal the Airbnb customer’s preferences and quantify the impact of non-market factors on the market price of tourist accommodation in Berlin, Germany. The data retrieved from Airbnb listings, publicly available on Inside Airbnb (2017), was supplemented on indicator of sharing economy accommodation using machine learning method in order to distinguish between amateur and business-running professional hosts. The main aim is to examine the consumers’ preferences and quantify the marginal effect of "real sharing economy" accommodation and other key variables on market price. This is accomplished by model approach using hedonic pricing method, which is used to estimate the economic value of particular attribute. Surprisingly, our data indicates the negative impact of sharing economy indicator on price. The set of motivations of consumers, which determine their valuation of Airbnb listings, was identified. The trade-off between encompass and parsimony of the set was desired in order to build an effective model. Calculation of proportion of explained variance showed that the price is affected mainly by number of accommodated persons, degree of privacy, number of bedrooms, cancellation policy, distance from the city centre and sharing economy indicator in decreasing order.
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Cankaya, Ergin Cagatay. "Testing methods for calibrating Forest Vegetation Simulator (FVS) diameter growth predictions." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/97321.

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The Forest Vegetation Simulator (FVS) is a growth and yield modeling system widely-used for predicting stand and tree-level attributes for management and planning applications in North American forests. The accuracy of FVS predictions for a range of tree and stand level attributes depends a great deal on the performance of the diameter increment model and its predictions of change in diameter at breast height (DBH) over time. To address the challenge of predicting growth in highly variable and geographically expansive forest systems, FVS was designed to include an internal calibration algorithm that makes use of growth observations, when available, from permanent inventory plots. The basic idea is that observed growth rates on a collection of remeasured trees are used to adjust or "calibrate" FVS diameter growth predictions. Therefore, DBH modeling was the focus of this investigation. Five methods were proposed for local calibration of individual tree DBH growth predictions and compared to two sets of results generated without calibration. Data from the US Forest Service's Forest Inventory and Analysis (FIA) program were used to test the methods for eleven widely-distributed forest tree species in Virginia. Two calibration approaches were based on median prediction errors from locally-observed DBH increments spanning a five year average time interval. Two were based on simple linear regression models fitted to the locally-observed prediction errors, and one method employed a mixed effects regression model with a random intercept term estimated from locally-observed DBH increments. Data witholding, specifically a leave-one-out cross-validation was used to compare results of the methods tested. Results showed that any of the calibration approaches tested in general led to improved accuracy of DBH growth predictions, with either of the median-based methods or regression based methods performing better than the random-effects-based approach. Equivalence testing showed that median or regression-based local calibration methods met error tolerances within ± 12% of observed DBH increments for all species with the random effects approach meeting a larger tolerance of ± 17%. These results showed improvement over uncalibrated models, which failed to meet tolerances as high as ± 30% for some species in a newly-fitted DBH growth model for Virginia, and as high as ± 170% for an existing model fitted to data from a much larger region of the Southeastern United States. Local calibration of regional DBH increment models provides an effective means of substantially reducing prediction errors when a relatively small set of observations are available from local sources such as permanent forest inventory plots, or the FIA database.
MS
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Robacker, Thomas C. "Comparison of Two Parameter Estimation Techniques for Stochastic Models." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2567.

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Parameter estimation techniques have been successfully and extensively applied to deterministic models based on ordinary differential equations but are in early development for stochastic models. In this thesis, we first investigate using parameter estimation techniques for a deterministic model to approximate parameters in a corresponding stochastic model. The basis behind this approach lies in the Kurtz limit theorem which implies that for large populations, the realizations of the stochastic model converge to the deterministic model. We show for two example models that this approach often fails to estimate parameters well when the population size is small. We then develop a new method, the MCR method, which is unique to stochastic models and provides significantly better estimates and smaller confidence intervals for parameter values. Initial analysis of the new MCR method indicates that this method might be a viable method for parameter estimation for continuous time Markov chain models.
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Luo, Hao. "Some Aspects on Confirmatory Factor Analysis of Ordinal Variables and Generating Non-normal Data." Doctoral thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-149423.

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This thesis, which consists of five papers, is concerned with various aspects of confirmatory factor analysis (CFA) of ordinal variables and the generation of non-normal data. The first paper studies the performances of different estimation methods used in CFA when ordinal data are encountered.  To take ordinality into account the four estimation methods, i.e., maximum likelihood (ML), unweighted least squares, diagonally weighted least squares, and weighted least squares (WLS), are used in combination with polychoric correlations. The effect of model sizes and number of categories on the parameter estimates, their standard errors, and the common chi-square measure of fit when the models are both correct and misspecified are examined. The second paper focuses on the appropriate estimator of the polychoric correlation when fitting a CFA model. A non-parametric polychoric correlation coefficient based on the discrete version of Spearman's rank correlation is proposed to contend with the situation of non-normal underlying distributions. The simulation study shows the benefits of using the non-parametric polychoric correlation under conditions of non-normality. The third paper raises the issue of simultaneous factor analysis. We study the effect of pooling multi-group data on the estimation of factor loadings. Given the same factor loadings but different factor means and correlations, we investigate how much information is lost by pooling the groups together and only estimating the combined data set using the WLS method. The parameter estimates and their standard errors are compared with results obtained by multi-group analysis using ML. The fourth paper uses a Monte Carlo simulation to assess the reliability of the Fleishman's power method under various conditions of skewness, kurtosis, and sample size. Based on the generated non-normal samples, the power of D'Agostino's (1986) normality test is studied. The fifth paper extends the evaluation of algorithms to the generation of multivariate non-normal data.  Apart from the requirement of generating reliable skewness and kurtosis, the generated data also need to possess the desired correlation matrices.  Four algorithms are investigated in terms of simplicity, generality, and reliability of the technique.
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Wikström, Gunilla. "Computation of Parameters in some Mathematical Models." Doctoral thesis, Umeå University, Computing Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-565.

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In computational science it is common to describe dynamic systems by mathematical models in forms of differential or integral equations. These models may contain parameters that have to be computed for the model to be complete. For the special type of ordinary differential equations studied in this thesis, the resulting parameter estimation problem is a separable nonlinear least squares problem with equality constraints. This problem can be solved by iteration, but due to complicated computations of derivatives and the existence of several local minima, so called short-cut methods may be an alternative. These methods are based on simplified versions of the original problem. An algorithm, called the modified Kaufman algorithm, is proposed and it takes the separability into account. Moreover, different kinds of discretizations and formulations of the optimization problem are discussed as well as the effect of ill-conditioning.

Computation of parameters often includes as a part solution of linear system of equations Ax = b. The corresponding pseudoinverse solution depends on the properties of the matrix A and vector b. The singular value decomposition of A can then be used to construct error propagation matrices and by use of these it is possible to investigate how changes in the input data affect the solution x. Theoretical error bounds based on condition numbers indicate the worst case but the use of experimental error analysis makes it possible to also have information about the effect of a more limited amount of perturbations and in that sense be more realistic. It is shown how the effect of perturbations can be analyzed by a semi-experimental analysis. The analysis combines the theory of the error propagation matrices with an experimental error analysis based on randomly generated perturbations that takes the structure of A into account

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Chu, Ka Lok 1975. "Inequalities and equalities associated with ordinary least squares and generalized least squares in partitioned linear models." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85140.

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The motivation for this thesis is the paper by Paul L. Canner [The American Statistician, vol. 23, no. 5, pp. 39--40 (1969)] in which it was noted that in simple linear regression it is possible for the generalized least squares regression line to lie either entirely above or entirely below all of the observed data points.
Chapter I builds on the observation that in Canner's model the ordinary least squares and generalized least squares regression lines are parallel, which led us to introduce a new measure of efficiency of ordinary least squares and to find conditions for which the total Watson efficiency of ordinary least squares in a partitioned linear model exceeds or is less than the product of the two subset Watson efficiencies, i.e., the product of the Watson efficiencies associated with the two subsets of parameters in the underlying partitioned linear model.
We introduce the notions of generalized efficiency function, efficiency factorization multiplier, and determinantal covariance ratio, and obtain several inequalities and equalities. We give special attention to those partitioned linear models for which the total Watson efficiency of ordinary least squares equals the product of the two subset Watson efficiencies. A key characterization involves the equality between the squares of a certain partial correlation coefficient and its associated ordinary correlation coefficient.
In Chapters II and IV we suppose that the underlying partitioned linear model is weakly singular in that the column space of the model matrix is contained in the column space of the covariance matrix of the errors in the linear model. In Chapter III our results are specialized to partitioned linear models where the partitioning is orthogonal and the covariance matrix of the errors is positive definite.
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Furlan, Camila Pedrozo Rodrigues. "Especificação do tamanho da defasagem de um modelo dinâmico." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4529.

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Several techniques are proposed to determine the lag length of a dynamic regression model. However, none of them is completely satisfactory and a wrong choice could imply serious problems in the estimation of the parameters. This dissertation presents a review of the main criteria for models selection used in the classical methodology and presents a way for determining the lag length from the perspective Bayesian. A Monte Carlo simulation study is conducted to compare the performance of the significance tests, R2 adjusted, final prediction error, Akaike information criterion, Schwarz information criterion, Hannan-Quinn criterion, corrected Akaike information criterion and fractional Bayesian approach. Two estimation methods are also compared, the ordinary least squares and the Almon approach.
Na literatura, muitas técnicas são propostas para determinar o tamanho da defasagem de um modelo de regressão dinâmico. Entretanto, nenhuma delas é completamente satisfatória e escolhas erradas implicam em sérios problemas na estimação dos parâmetros. Este trabalho apresenta uma revisão dos principais critérios de seleção de modelos disponíveis na metodologia clássica, assim como aborda uma maneira de determinar o tamanho da defasagem sob a perspectiva Bayesiana. Um estudo de simulação Monte Carlo é conduzido para comparar a performance dos testes de significância, do R2 ajustado, do erro de predição final, dos critérios de informação de Akaike, Schwarz, Hannan-Quinn e Akaike corrigido e da aproximação Bayesiana fracionada. Também serão comparados os métodos de estimação de Mínimos Quadrados Ordinários e de Almon.
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Books on the topic "Ordinary least squares method"

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Jiang, Bo-nan. The Least-Squares Finite Element Method. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03740-9.

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Wu, Sean F. The Helmholtz Equation Least Squares Method. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1640-5.

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D, Gunzburger Max, ed. Least-squares finite element methods. New York: Springer, 2009.

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Bochev, Pavel B. Least-squares finite element methods. New York: Springer, 2009.

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Jiang, Bo-Nan. Least-squares finite elements for Stokes problem. Cleveland, Ohio: ICOMP, 1988.

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Merriman, Mansfield. Elements of the method of least squares. [Place of publication not identified]: Nabu Press, 2010.

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Bartlett, Dana P. General principles of the method of least squares. Mineola, NY: Dover, 2006.

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Bochev, Pavel B. Least-squares finite element methods. New York: Springer, 2009.

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Jiang, Bo-nan. Least-squares finite element method for fluid dynamics. Cleveland, Ohio: Institute for Computational Mechanics in Propulsion, 1989.

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Kirkeby, O. Wavefront reconstruction using a least squares approach. Southampton, England: University of Southampton, Institute of Sound and Vibration, 1992.

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Book chapters on the topic "Ordinary least squares method"

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Balzer, W., and E. W. Haendler. "Ordinary Least Squares as a Method of Measurement." In Philosophy of Economics, 129–46. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2319-5_8.

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Wooditch, Alese, Nicole J. Johnson, Reka Solymosi, Juanjo Medina Ariza, and Samuel Langton. "Ordinary Least Squares Regression." In A Beginner’s Guide to Statistics for Criminology and Criminal Justice Using R, 245–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50625-4_15.

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Demaison, Jean, and Natalja Vogt. "Least-Squares Method." In Lecture Notes in Chemistry, 233–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60492-9_9.

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Gooch, Jan W. "Least Squares Method." In Encyclopedic Dictionary of Polymers, 985–86. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15272.

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Uncini, Aurelio. "Least Squares Method." In Fundamentals of Adaptive Signal Processing, 143–204. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02807-1_4.

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Malley, James D. "The Ordinary Least Squares Estimates." In Optimal Unbiased Estimation of Variance Components, 29–35. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_4.

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Praag, M. S. "Probit Ordinary Least Squares (POLS)." In Encyclopedia of Quality of Life and Well-Being Research, 5072–73. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_3328.

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Praag, M. S. Van B. "Cardinal Ordinary Least Squares (COLS)." In Encyclopedia of Quality of Life and Well-Being Research, 536–37. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_3329.

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Zdaniuk, Bozena. "Ordinary Least-Squares (OLS) Model." In Encyclopedia of Quality of Life and Well-Being Research, 4515–17. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2008.

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Sydsæter, Knut, Arne Strøm, and Peter Berck. "Method of least squares." In Economists’ Mathematical Manual, 207–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-28518-2_35.

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Conference papers on the topic "Ordinary least squares method"

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Nguyen, Duong T. "Parametric Identification of Electric Drives Using the Ordinary Least Squares Method." In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. http://dx.doi.org/10.1109/elconrus51938.2021.9396566.

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Gro¨nstedt, Tomas. "Least Squares Based Transient Nonlinear Gas Path Analysis." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68717.

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A method for computing least squares estimates for transient nonlinear gas path analysis is derived. The solution to the optimal estimation problem is found by solving a system of nonlinear equations. A single iteration of the equation system requires integrating an extended set of nonlinear ordinary differential algebraic equations (ODAE). The additional differential equations originate from the differentiation of the least squares expression used to define optimality. The numerical efficiency of the extended ODAE algorithm is assessed by comparing it to an optimization based method. To illustrate the derived estimation technique a complete model of the Frank Whittle W1 engine is given within the paper. An example of the implementation of the extended ODAE method is demonstrated in the framework of this model. The performance of the method is also discussed and evaluated on a full nonlinear transient model of the RM12 fighter engine.
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"Regional flood modelling in Western Australia: Application of regression based methods using ordinary least squares." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.i8.taylor.

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Alciatore, David G., and Henry P. Miranda. "The Best Least-Squares Line Fit." In ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium collocated with the ASME 1994 Design Technical Conferences. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/cie1994-0432.

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Abstract This paper presents a method for determining the least-squares best-fit line through a set of two-dimensional data. The algorithm is orientation independent and does not rely on knowledge of ordinate and abscissa accuracy. To simplify the analysis, the best-fit line is described by two parameters: q, the angle from the horizontal axis, and r, the perpendicular distance from the origin. This parameterization results in general robust solution resulting in no degenerate cases or indeterminacies. The final results are presented in the standard form, Ax + By = C.
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Siami, A., and M. Farid. "Identification and Defect Detection of Continuous Dynamic Systems." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14364.

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This paper presents a systematic and efficient algorithm using a coupled finite element - finite difference - least square method for identification and defect detection of continuous system using dynamic response of such systems. First the governing partial differential equations of motion of continuous systems such as beams are reduced to a set of ordinary differential equations in time domain using finite elements. Then finite difference method is used to convert these equations into a set of algebraic equations. This set of equations is considered as a set of equality constraints of an optimization problem in which the objective function is the summation of the squares of differences between measured data at specific points and the predicted data obtained by the solution of the governing system of differential of equations. This method has been successfully applied to find mechanical properties of aforementioned systems in an iterative procedure.
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Esaa, Ayat Abdelrahim Suliman, Harun Bal, and Erhan İşcan. "The Export-Led Growth Hypothesis: A Panel Cointegration Approach in the Middle East and North Africa Countries (1980-2017)." In International Conference on Eurasian Economies. Eurasian Economists Association, 2019. http://dx.doi.org/10.36880/c11.02296.

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This study examines the hypothesis of the Export-Led Growth in the seven selected Middle East and North Africa countries, the hypothesis state that export growth driven by export promotion policies enhances overall economic growth. Empirical investigations have tended to focus attention on the direction of causality between exports and economic growth using Granger causality tests. However, the empirical results based on these tests are, at best, mixed and often contradictory. The paper employs panel data analysis by utilizing the Pedroni panel cointegration, Pedroni Dynamic Ordinary Least Squares and Fully Modify Ordinary Least Squares, and Canning-Pedroni causality methods, a recent development in panel data econometrics, properties of integration and cointegration and consistency of parameters. The study considers the following three variables; Real Gross Domestic Product (GDP), Real exports (EXP) and Real import (IMP). Annual secondary data are obtained from the World Bank Development Indicator for seven MENA countries, Namely, Algeria, Egypt, Sudan, Jordan, Saudi Arabia, UAE, and Qatar. The empirical results emphasize the existence of a positive relationship between Export and GDP. Results of waled and Z-bar Group statistics indicate the long-run unidirectional causality between Export and GDP, operates from Export to the GDP. It confirms the validity of Export-led growth hypothesis of the seven selected MENA countries. Empirical evidence suggests significant policy prescriptions; these countries should focus more on supporting export orientated industries through aid-for-trade, trade-capacity building schemes and other types of policies in order to promote economic growth.
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Hays, Joe, Adrian Sandu, Corina Sandu, and Dennis Hong. "Parametric Design Optimization of Uncertain Ordinary Differential Equation Systems." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62789.

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This work presents a novel optimal design framework that treats uncertain dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. The inclusion of uncertainty in design is of paramount practical importance because all real-life systems are affected by it. Designs that ignore uncertainty often lead to poor robustness and suboptimal performance. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The uncertainty statistics are explicitly included in the optimization process. Systems that are nonlinear, have active constraints, or opposing design objectives are shown to benefit from the new framework. Specifically, using a constraint-based multi-objective formulation, the direct treatment of uncertainties during the optimization process is shown to shift, or off-set, the resulting Pareto optimal trade-off curve. A nonlinear vehicle suspension design problem, subject to parametric uncertainty, illustrates the capability of the new framework to produce an optimal design that accounts for the entire family of systems within the associated probability space.
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Özer, Ali, Aslı Cansın Doker, and Adem Türkmen. "Analysis of Capital Flight in Developing Countries: A Study on Turkey between 1980 and 2010." In International Conference on Eurasian Economies. Eurasian Economists Association, 2013. http://dx.doi.org/10.36880/c04.00702.

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The aim of this study is to determine whether there is a relationship between Capital flight and some macroeconomic variables by using anual data between 1980 and 2010 in Turkey. Capital flight measured by World Bank (1985) method, was used as dependent variable and external debt, foreign direct investment, uncertainty, real GDP growth, exchange rates, trade balance and consumer price index were used as independent variables. Ordinary Least squares estimation method, Johansen-Jeselius cointegration test, Granger causality test and variance decomposition results produced by VEC model were used in the study. After those econometrics and economics analysis, this paper put forward that there is a long run relationship between some macroeconomic variables and capital flight.The results show external debt, foreign direct investment inflows, and foreign reserves to be the major effector of capital flight.
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Noveski, Martin, Nina Mojsova Kjoseva, and Sasho Kjosev. "GOVERNMENT INDEBTEDNESS AND ECONOMIC GROWTH IN THE REPUBLIC OF NORTH MACEDONIA." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0001.

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Government consumption plays an important role for stability of the national economy, especially in periods of economic crisis. However, rapidly growing public debt is a concerning issue nowadays, since it might jeopardize economic growth perspectives. Economic theory suggests that public debt has non-linear impact on economic growth in a form of inverted U-shape. In other words, it is believed that after a certain threshold, public debt will have negative impact on economic growth. Given that such threshold varies significantly across countries, the aim of this paper is to calculate the turning point of the public debt impact in the Republic of North Macedonia. For this purpose, we use non-linear multiple regression model for real GDP growth rate as dependent variable, general government public debt-to-GDP ratio (in nominal and squared terms) as key independent variable, as well as several other controlling variables. Since theory also suggests reverse causality between economic growth and public debt, we use three different estimation techniques (Ordinary Least Squares, Two-Stages Least Squares, and Generalized Method of Moments) to deal with potential endogeneity, as well as to cross-validate the results. Our results show that general government debt in the Republic of North Macedonia positively affects economic growth until it reaches around 30% of GDP, whereas further indebtedness after that turning point will most likely have negative impact. Given that current debt level is slightly above 40% (10 percentage points higher than the turning point), whereby due to the COVID-19 crisis it is expected to grow even more in the upcoming years, the need of urgent fiscal consolidation inevitably arises. In this regards, deeper and more comprehensive analysis is needed in order to identify adequate channels for its efficient and effective implementation.
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Tu, Ying, Michael Muskulus, and Thorvald C. Grindstad. "Two Methods for the Inverse Estimation of Local Slamming Loads on a Jacket Structure." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54462.

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This article illustrates two inverse methods to estimate the local slamming forces on a jacket structure. The experimental data from the hammer test and the wave test are used as the inputs. One method uses two deconvolution techniques: a conjugate gradient technique to solve the impulse response functions and a weighted eigenvector expansion technique to reconstruct the wave impact forces. The other method uses linear regression with the ordinary least square technique to estimate the wave impact forces. The results calculated with these two different methods are highly identical, which enhances the confidence in the result accuracy. The time series of the reconstructed forces are detailed at a millisecond level, which provides decent information on the shape of the forces. This capability enables the methods to be a very useful tool for the further investigations of the local slamming forces.
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Reports on the topic "Ordinary least squares method"

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Cumby, Robert, and John Huizinga. Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions. Cambridge, MA: National Bureau of Economic Research, October 1990. http://dx.doi.org/10.3386/t0092.

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Chervenkov, Hristo, and Kiril Slavov. Theil–Sen Estimator vs. Ordinary Least Squares — Trend Analysis for Selected ETCCDI Climate Indices. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/crabs.2019.01.06.

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Dennis, Jr, Songbai J. E., Vu Sheng, and Phuong A. A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada454936.

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GOSSLER, ALBERT A. Moving Least-Squares: A Numerical Differentiation Method for Irregularly Spaced Calculation Points. Office of Scientific and Technical Information (OSTI), June 2001. http://dx.doi.org/10.2172/782717.

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GOSSLER, ALBERT A. Moving Least-Squares: A Numerical Differentiation Method for Irregularly Spaced Calculation Points. Office of Scientific and Technical Information (OSTI), June 2001. http://dx.doi.org/10.2172/782718.

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Kevorkian, A. K. A Direct Decomposition Method for the Solution of Sparse Linear Least Squares Problems. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada284060.

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Yao, Stephen E., Fred McCartney Dickey, and Sara North Pecak. A least squares method for CVT calibration in a RLC capacitor discharge circuit. Office of Scientific and Technical Information (OSTI), November 2003. http://dx.doi.org/10.2172/918301.

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Reister, D. B., and M. D. Morris. A method for obtaining a least squares fit of a hyperplane to uncertain data. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/10153960.

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Koester, Jacob, Michael R. Tupek, and Scott A. Mitchell. An Agile Design-to-Simulation Workflow Using a New Conforming Moving Least Squares Method. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1569655.

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Dennis, J. E., H. J. Martinez, and R. A. Tapia. A Convergence Theory for the Structured BFGS Secant Method With an Application to Nonlinear Least Squares. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada455135.

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