Relevant bibliographies by topics / Generalized least square (GLS)

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

Academic literature on the topic 'Generalized least square (GLS)'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Generalized least square (GLS)"

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R, Aditya Setyawan, Mustika Hadijati, and Ni Wayan Switrayni. "Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi." EIGEN MATHEMATICS JOURNAL 1, no. 2 (December 31, 2019): 61. http://dx.doi.org/10.29303/emj.v1i2.43.

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Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.
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Rashid, Intan Maizura Abd. "Determinants of FDI Inflows in Agriculture Sector Using Pooled Ordinary Least Square (OLS), Pooled Generalized Least Square (GLS), Augmented Dickey-Fuller (ADF) and Philips-perron Unit Root Test." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 2560–67. http://dx.doi.org/10.37200/ijpr/v24i5/pr201955.

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Angriany, A. Muthiah Nur, Georgina Maria Tinungki, and Raupong Raupong. "Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 20, 2018): 54. http://dx.doi.org/10.20956/jmsk.v15i2.5714.

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AbstractsExperiment design is a test or a row of test by using both statistical description and inference statistical. The aim of this test is to change an input to become an output as a respond of the experiment. In the experiment design, variance of factor A, B , AB error of variance are called as variant component. The aim of this study is to estimate variance component on complete random factorial design for fixed model and mixed model by using Generalized Least Squares (GLS)method, where GLS method as a development of Ordinary Least Square method. It used to be applied on data of complete random factorial design, namely like the influence to density pelleting food which is caused by increasing adhesive material and longtime in storage. The results show that there is no influence of increasing adhesive material to the density of pelleting food. In addition, there exist of diversity of longtime of storage and there exists a diversity interaction between adding adhesive material and long of time of storage to the density of pelleting food Keywords: Generalized Least Squares, variance component, complete random factorial design AbstrakPerancangan percobaan adalah suatu uji atau sederet uji baik itu menggunakan statistika deskripsi maupun statistika inferensi, yang bertujuan untuk mengubah peubah input menjadi suatu output yang merupakan respon dari percobaan tersebut. Dalam perancangan percobaan, variansi dari faktor A, variansi dari faktor B, variansi interaksi faktor AB, dan variansi galat disebut dengan komponen varian. Penelitian ini bertujuan untuk mengestimasi komponen variansi pada rancangan faktorial acak lengkap model tetap dan model campuran menggunakan metode Generalized Least Squares (GLS), dimana metode GLS adalah pengembangan dari metode Ordinary Least Square yang biasa digunakan untuk mengatasi asumsi homogenitas yang biasa dilanggar dalam perancangan percobaan. Metode tersebut diterapkan pada data rancangan faktorial acak lengkap yaitu pengaruh berat jenis pakan pellet dengan kombinasi perlakuan penambahan bahan perekat dan lama penyimpanan. Hasil menunjukkan bahwa tidak terdapat pengaruh penambahan bahan perekat terhadap berat jenis pakan pellet. Selain itu, terdapat keragaman faktor lama penyimpan dan terdapat keragaman interaksi antara faktor penambahan perekat dan lama penyimpanan terhadap berat jenis pakan pellet. Kata kunci: Generalized Least Squares, komponen variansi, rancangan faktorial acak lengkap
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Yuan, Ke-Hai, and Yutaka Kano. "Meta-Analytical SEM: Equivalence Between Maximum Likelihood and Generalized Least Squares." Journal of Educational and Behavioral Statistics 43, no. 6 (August 13, 2018): 693–720. http://dx.doi.org/10.3102/1076998618787799.

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Meta-analysis plays a key role in combining studies to obtain more reliable results. In social, behavioral, and health sciences, measurement units are typically not well defined. More meaningful results can be obtained by standardizing the variables and via the analysis of the correlation matrix. Structural equation modeling (SEM) with the combined correlations, called meta-analytical SEM (MASEM), is a powerful tool for examining the relationship among latent constructs as well as those between the latent constructs and the manifest variables. Three classes of methods have been proposed for MASEM: (1) generalized least squares (GLS) in combining correlations and in estimating the structural model, (2) normal-distribution-based maximum likelihood (ML) in combining the correlations and then GLS in estimating the structural model (ML-GLS), and (3) ML in combining correlations and in estimating the structural model (ML). The current article shows that these three methods are equivalent. In particular, (a) the GLS method for combining correlation matrices in meta-analysis is asymptotically equivalent to ML, (b) the three methods (GLS, ML-GLS, ML) for MASEM with correlation matrices are asymptotically equivalent, (c) they also perform equally well empirically, and (d) the GLS method for SEM with the sample correlation matrix in a single study is asymptotically equivalent to ML, which has being discussed extensively in the SEM literature regarding whether the analysis of a correlation matrix yields consistent standard errors and asymptotically valid test statistics. The results and analysis suggest that a sample-size weighted GLS method is preferred for combining correlations and for MASEM.
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Angriany, A. Muthiah Nur, Georgina Maria Tinungki, and Raupong Raupong. "Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 6, 2018): 52. http://dx.doi.org/10.20956/jmsk.v15i2.5569.

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Perancangan percobaan adalah suatu uji atau sederet uji baik itu menggunakan statistika deskripsi maupun statistika inferensi, yang bertujuan untuk mengubah peubah input menjadi suatu output yang merupakan respon dari percobaan tersebut. Dalam perancangan percobaan, variansi dari faktor A, variansi dari faktor B, variansi interaksi faktor AB, dan variansi galat disebut dengan komponen varian. Penelitian ini bertujuan untuk mengestimasi komponen variansi pada rancangan faktorial acak lengkap model tetap dan model campuran menggunakan metode Generalized Least Squares (GLS), dimana metode GLS adalah pengembangan dari metode Ordinary Least Square yang biasa digunakan untuk mengatasi asumsi homogenitas yang biasa dilanggar dalam perancangan percobaan. Metode tersebut diterapkan pada data rancangan faktorial acak lengkap yaitu pengaruh berat jenis pakan pellet dengan kombinasi perlakuan penambahan bahan perekat dan lama penyimpanan. Hasil menunjukkan bahwa tidak terdapat pengaruh penambahan bahan perekat terhadap berat jenis pakan pellet. Selain itu, terdapat keragaman faktor lama penyimpan dan terdapat keragaman interaksi antara faktor penambahan perekat dan lama penyimpanan terhadap berat jenis pakan pellet.
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Ammari, Mohamed Lassaad, and Paul Fortier. "Analysis of MIMO Receiver Using Generalized Least Squares Method in Colored Environments." Journal of Computer Networks and Communications 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/720546.

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The classical detection techniques for multiple-input multiple-output (MIMO) systems are usually designed with the assumption that the additive complex Gaussian noise is uncorrelated. However, for closely spaced antennas, the additive noise is correlated due to the mutual antenna coupling. This letter analyzes an improved zero-forcing (ZF) technique for MIMO channels in colored environments. The additive noise is assumed to be correlated and the Rayleigh MIMO channel is considered doubly correlated. The improved ZF detector, based on the generalized least squares estimator (GLS), takes into account the noise covariance matrix and provides an unbiased estimator of the transmitted symbol vectors. We introduce some novel bounds on the achievable sum rate, on the normalized mean square error at the receiver output, and on the outage probability. The derived expressions are compared to Monte Carlo simulations.
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Prastuti, Mike, and Iis Dewi Ratih. "KAJIAN SIMULASI ESTIMASI PARAMETER MODEL GSTAR-GLS UNTUK DATA BERPOLA MUSIMAN." MEDIA BINA ILMIAH 13, no. 12 (July 23, 2019): 1769. http://dx.doi.org/10.33758/mbi.v13i12.261.

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Secara umum, metode untuk mengestimasi parameter dalam model GSTAR adalah Ordinary Least Squares (OLS). Estimasi parameter dengan menggunakan OLS untuk model GSTAR dengan residual yang berkorelasi akan menghasilkan estimator yang tidak efisien. Metode yang sesuai untuk mengestimasi parameter dengan residual yang berkorelasi adalah Generalized Least Square (GLS). Tujuan dari makalah ini adalah untuk mengusulkan metode GLS untuk mengestimasi parameter dalam model GSTAR Musiman, yang dikenal sebagai GSTAR-GLS, dan membandingkan hasilnya dengan metode OLS atau GSTAR-OLS. Selain itu, tujuan dari makalah ini adalah untuk mengekaji lebih lanjut dalam penentuan bobot spasial yang sesuai pada model GSTAR. Hasil studi simulasi menunjukkan bahwa penentuan bobot spasial pada model GSTAR dapat dilakukan secara optimal dengan menggunakan normalisasi hasil inferensi statistik terhadap parsial korelasi silang antar lokasi pada lag waktu yang bersesuaian. Selain itu, GSTAR-GLS menghasilkan estimator yang lebih efisien daripada GSTAR-OLS, dimana standard error yang dihasilkan lebih kecil
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Roch, Sebastien, and Karl Rohe. "Generalized least squares can overcome the critical threshold in respondent-driven sampling." Proceedings of the National Academy of Sciences 115, no. 41 (September 25, 2018): 10299–304. http://dx.doi.org/10.1073/pnas.1706699115.

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To sample marginalized and/or hard-to-reach populations, respondent-driven sampling (RDS) and similar techniques reach their participants via peer referral. Under a Markov model for RDS, previous research has shown that if the typical participant refers too many contacts, then the variance of common estimators does not decay like O(n−1), where n is the sample size. This implies that confidence intervals will be far wider than under a typical sampling design. Here we show that generalized least squares (GLS) can effectively reduce the variance of RDS estimates. In particular, a theoretical analysis indicates that the variance of the GLS estimator is O(n−1). We then derive two classes of feasible GLS estimators. The first class is based upon a Degree Corrected Stochastic Blockmodel for the underlying social network. The second class is based upon a rank-two model. It might be of independent interest that in both model classes, the theoretical results show that it is possible to estimate the spectral properties of the population network from a random walk sample of the nodes. These theoretical results point the way to entirely different classes of estimators that account for the network structure beyond node degree. Diagnostic plots help to identify situations where feasible GLS estimators are more appropriate. The computational experiments show the potential benefits and also indicate that there is room to further develop these estimators in practical settings.
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Affandi, Affandi, and Eddy Gunawan. "PENGARUH EKSPOR, IMPOR DAN JUMLAH PENDUDUK TERHADAP PDB INDONESIA TAHUN 1969 -2016." JURNAL PERSPEKTIF EKONOMI DARUSSALAM 4, no. 2 (July 1, 2019): 249–64. http://dx.doi.org/10.24815/jped.v4i2.13021.

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This research aims to know the influence of export, import and population against Indonesia gross domestic product. The data used in this research is secondary data from the years 1969-2016 were sourced from a variety of reports and the compilation of the particular publication of the World Bank. The model used was multiple linear regression analysis method using the approach of Generalized Least Square parameter estimation (GLS). The results of calculations indicate that the variable is positive and significant effect of exports to GDP, population of Indonesia a negative and significant effect against Indonesia'S GDP, while imports of influential positive and insignificant to GDP Indonesian. The value of the coefficient of determination (R2 = 0.9464 adj.)show that Indonesia'S GDP amounted to 94.64 percent affected by the Export, import and Population, while the remaining 5.36 percent affected by factors other than this research.Keywords : Import, Export, Population, Gross Domestic Product, Generalized Least SquareAbstrakPenelitian ini bertujuan untuk mengetahui pengaruh ekspor, impor dan jumlah penduduk terhadap produk domestik bruto Indonesia. Data yang digunakan dalam penelitian ini adalah data sekunder dari tahun 1969-2016 yang bersumber dari berbagai laporan dan kompilasi khususnya publikasidari World Bank. Model yang digunakan adalah regresi linear berganda dengan metode analisis menggunakan pendekatan estimasi parameter Generalized Least Square (GLS). Hasil perhitungan menunjukkan bahwa variabel ekspor berpengaruh positif dan signifikan terhadap PDB Indonesia, jumlah penduduk berpengaruh negatif dan signifikan terhadap PDB Indonesia, sedangkan imporberpengaruh positif dan tidak signifikan terhadap PDB Indonesia. Nilai koefisien determinasi (Adj.R2= 0.9464) Menunjukkan bahwa PDB Indonesia sebesar 94,64 persen dipengaruhi oleh Ekspor, Impor dan Jumlah Penduduk, sedangkan sisanya 5,36 persen dipengaruhi oleh faktor-faktor lain diluar penelitian ini.Kata Kunci : Impor, Ekspor, Jumlah Penduduk, Produk Domestik Bruto, Generalized Least Square
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Nisak, Siti Choirun. "Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts." CAUCHY 4, no. 2 (June 3, 2016): 57. http://dx.doi.org/10.18860/ca.v4i2.3488.

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Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR) is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS) is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS) assumes the variance-covariance matrix has a constant error 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝝈𝟐) but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR) approach is used to accommodate the weakness of the OLS. SUR assumption is 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝚺) for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS). Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1)(1,12,36),(0),(1))-SUR with determination coefficient generated with the average of 57.726%.
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Dissertations / Theses on the topic "Generalized least square (GLS)"

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Wang, Shuo. "An Improved Meta-analysis for Analyzing Cylindrical-type Time Series Data with Applications to Forecasting Problem in Environmental Study." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/386.

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This thesis provides a case study on how the wind direction plays an important role in the amount of rainfall, in the village of Somi$acute{o}$. The primary goal is to illustrate how a meta-analysis, together with circular data analytic methods, helps in analyzing certain environmental issues. The existing GLS meta-analysis combines the merits of usual meta-analysis that yields a better precision and also accounts for covariance among coefficients. But, it is quite limited since information about the covariance among coefficients is not utilized. Hence, in my proposed meta-analysis, I take the correlations between adjacent studies into account when employing the GLS meta-analysis. Besides, I also fit a time series linear-circular regression as a comparable model. By comparing the confidence intervals of parameter estimates, covariance matrix, AIC, BIC and p-values, I discuss an improvement on the GLS meta analysis model in its application to forecasting problem in Environmental study.
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Wu, Ruochen. "Essays on semi-parametric Bayesian econometric methods." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/288745.

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This dissertation consists of three chapters on semi-parametric Bayesian Econometric methods. Chapter 1 applies a semi-parametric method to demand systems, and compares the abilities to recover the true elasticities of different approaches to linearly estimating the widely used Almost Ideal demand model, by either iteration or approximation. Chapter 2 co-authored with Dr. Melvyn Weeks introduces a new semi-parametric Bayesian Generalized Least Square estimator, which employs the Dirichlet Process prior to cope with potential heterogeneity in the error distributions. Two methods are discussed as special cases of the GLS estimator, the Seemingly Unrelated Regression for equation systems, and the Random Effects Model for panel data, which can be applied to many fields such as the demand analysis in Chapter 1. Chapter 3 focuses on the subset selection for the efficiencies of firms, which addresses the influence of heterogeneity in the distributions of efficiencies on subset selections by applying the semi-parametric Bayesian Random Effects Model introduced in Chapter 2.
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Mbah, Alfred Kubong. "On the theory of records and applications." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002216.

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"The generalized least square estimation of polychoric correlation." Chinese University of Hong Kong, 1985. http://library.cuhk.edu.hk/record=b5885574.

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Skatulla, Sebastian. "Computational aspects of generalized continua based on moving least square approximations." 2006. http://hdl.handle.net/2440/59012.

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In recent years, current engineering technology lead to a renewed interest in generalized continuum theories. In particular, generalized continua are able to address fundamental physical phenomena which are related to the underlying microstructure of the material. Specifically scale-effects are of special interest. In this work a generalized deformation formulation is developed which allows to incorporate material information from the microscopic and the macroscopic space into an unified constitutive model. The approach is based on a theory developed by Sansour (1998) which was originated in theoretical considerations of Ericksen and Truesdell (1957) and later on Eringen and his co-workers (Eringen 1999). The basic idea is to construct a generalized continuum consisting of macro- and micro-continuum and subsequently to compose the generalized deformation by a macro- and micro-component. This procedure results in a generalized problem formulation. Furthermore, new strain measures as well as corresponding field equations can be identified. Here, it is assumed that the deformation field can only be varied within the macro-continuum so that the balance equations are established for the macro-space. The constitutive law is defined at the microscopic level and the geometrical specification of the micro-continuum is the only material input which goes beyond those needed in a classical description. A special detail of this approach is that it involves first order strain gradients which are expressed by second order derivatives of the field. It allows to address relative motion between micro- and macro-space without adding extra degrees of freedom. In order to model this formulation this work makes use of a meshfree method based on moving least squares (MLS) which is able to provide the required higher order continuity (Lancaster and Salkauskas 1981). Examples of meshfree methods are the diffuse element method (Nayroles, Touzot, and Villon 1992), the element-free Galerkin method (Belytschko, Lu and Gu 1994), the reproducing kernel particle method (Liu and Chen, 1995), the partition of unity method (Melenk and Babuska 1997) and the hp-cloud method (Duarte and Oden 1996), just to name a few. It was demonstrated that these kind of methods can deal especially well with problems which are characterized by large deformation or changing domain geometry. The potential in modelling formulations involving higher order derivatives has not been widely recognized yet, with the exception of a few one- respectively two-dimensional case studies (Tang et al., 2003). This work now aims to illustrate the excellent applicability of the proposed generalized deformation formulation in combination with MLS by modelling elastic and plastic problems which are proven to exhibit size-scale effects (Yang and Lakes 1981 ; Fleck et al. 1994 ; Aifantis 1999 ; Lam et al. 2003). Furthermore, a large-scale case study on underground excavation design reveals the potential and adaptivity of this theory with respect to heterogeneous material such as rock.
Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2006.
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劉家銓. "A generalized least square formulation for multi-period optimization of asset allocation." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/68934980600308859989.

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碩士
國立政治大學
風險管理與保險研究所
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This paper deals with the insurance and pension asset liability management issue. Huang (2004) derives a theoretical close solution of multi-period asset allocation. However, there are two further problems in his paper. First, short selling is allowable. Second, multi-period investing is not acceptable. These two restrictions sometimes are big problems in practice. This paper extends his paper and releases these two restrictions. In other words, we intend to find a solution of multi-period asset allocation so that we can invest money and change proportion of investment in each period without problems of short selling. In this paper, we use the standard asset classes used by pension or insurance funds such as short-term bonds, consols, index-linked gilts and equities. We generate thousand times of Monte Caro simulations of Wilkie investment model (1995) to predict future asset returns. Furthermore, in order to improve time-efficiency and accuracy, we derive a quadratic objective function and obtain a unique solution using sequential quadratic programming.
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Wang, Shanshan. "Bootstrapping generalized two stage least square estimates in simultaneous equation model with both fixed and random coefficients." Thesis, 1990. http://spectrum.library.concordia.ca/2634/1/ML56033.pdf.

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Books on the topic "Generalized least square (GLS)"

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Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

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This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.
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Book chapters on the topic "Generalized least square (GLS)"

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Pan, Jian-Xin, and Kai-Tai Fang. "Generalized Least Square Estimation." In Growth Curve Models and Statistical Diagnostics, 38–76. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21812-0_2.

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Chan, Kit Yan, C. K. Kwong, and Tharam S. Dillon. "Generalized Fuzzy Least Square Regression for Generating Customer Satisfaction Models." In Computational Intelligence Techniques for New Product Design, 129–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27476-3_7.

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Cen, Song, and Zhi-Fei Long. "Generalized Conforming Thin Plate Element III — Perimeter-Point and Least-Square Conforming Schemes." In Advanced Finite Element Method in Structural Engineering, 176–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00316-5_7.

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Li, Haoqi. "Generalized Least Square Estimation of Error-in-Variable Models and its Statistical Property." In Lecture Notes in Electrical Engineering, 1490–95. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_200.

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Nithya, C., and V. Saravanan. "Generalized Least Square Feature Engineering-Based Weighted Gradient Boost SVM Classifier for Medical Data Diagnosis." In Smart Intelligent Computing and Applications, 269–86. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9282-5_25.

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Sorman, A. U. "Regional Streamflow Network Analysis Using the Generalized Least Square Method: A Case Study in The Kizilirmak River Basin." In Integrated Technologies for Environmental Monitoring and Information Production, 91–102. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0231-8_8.

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"Generalized Least Squares (GLS): Extensions of the Classical Linear Regression Model." In Linear Regression: A Mathematical Introduction, 91–110. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc, 2019. http://dx.doi.org/10.4135/9781071802571.n5.

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Taneja, Abhishek. "Enhancing Web Data Mining." In Advances in Data Mining and Database Management, 116–36. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch005.

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An enormous production of databases in almost every area of human endeavor particularly through web has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. The aim of this study is to study the predictive ability of Factor Analysis a web mining technique to prevent voting, averaging, stack generalization, meta- learning and thus saving much of our time in choosing the right technique for right kind of underlying dataset. This chapter compares the three factor based techniques viz. principal component regression (PCR), Generalized Least Square (GLS) Regression, and Maximum Likelihood Regression (MLR) method and explores their predictive ability on theoretical as well as on experimental basis. All the three factor based techniques have been compared using the necessary conditions for forecasting like R-Square, Adjusted R-Square, F-Test, JB (Jarque-Bera) test of normality. This study can be further explored and enhanced using sufficient conditions for forecasting like Theil's Inequality coefficient (TIC), and Janur Quotient (JQ).
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Ben Romdhane, Youssra, Sahar Loukil, and Souhaila Kammoun. "Economic African Development in the Context of FinTech." In Advances in Electronic Government, Digital Divide, and Regional Development, 273–89. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1851-9.ch014.

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The purpose of this chapter is to analyze the effect of FinTech and political incertitude on economic growth through a multiple regression. Thus, the authors employ the method of generalized least square (GLS) with panel data. The sample concerns 21 African countries during (2001-2014-2017). The authors use a wide range of measures from Global Findex Database 2017, the World Bank platform, the World Bank national accounts data, and the OECD National Accounts data files base in the context of Africa. Empirical results show that FinTech is a driver of economic growth unless it is actively used in a developed digital infrastructure. In fact, the authors prove that, when financial technologies are used in both transactions (receive and made digital payment), they significantly contribute to the economic cycle. Passive use like simple consumption actions are not a significant lever for the economy. The principal contribution is to highlight that the active use of financial innovations and not passive one and the developed digital infrastructure do promote economic growth in African countries.
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Lee, Youngjo, Lars Rönnegård, and Maengseok Noh. "GLMs via Iterative Weighted Least Squares." In Data Analysis Using Hierarchical Generalized Linear Models With R, 19–36. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315211060-2.

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Conference papers on the topic "Generalized least square (GLS)"

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Velni, J. M., and K. Khorasani. "Localization of wideband sources in colored noise VIA generalized least squares (GLS)." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628651.

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Martinez-Flores, Rene, Achintya Haldar, and Hasan Katkhuda. "Structural Health Assessment After an Impact." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-13718.

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An innovative technique to assess structural health just after subjected to impulsive loadings (blasts, explosions, etc.) underdevelopment at the University of Arizona was experimentally verified and is presented in this paper. The authors called it the Generalized Iterative Least Square Extended Kalman Filter with Unknown Input (GILS-EKF-UI) method. The system is represented by finite elements and a Kalman filter-based system identification (SI) technique is used to identify the system. Some of the major characteristics of the method are that it does not require information on input excitation and can identify a system with limited noise-contaminated response information measured at few node points. To implement the Kalman-filter based algorithm, the information on the input excitation and the initial state vector must be available. The authors proposed a two-stage approach. In the first stage, based on the limited measured response information available at the locations of the sensors, a substructure is identified. After the completion of the first stage, the input excitation information that caused the responses and the stiffness of all the elements in the substructure can be evaluated. Then, in stage 2, the Kalman-filter based algorithm is used to identify the whole structure. The experimental verification of the method is emphasized in this paper.
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Li, Tongying, and Hongbo Zhu. "Design and Implementation of Autoleveller Algorithm Based on Generalized Least Square Method." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996967.

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Laili, Masnatul, Bambang Widjanarko Otok, and Vita Ratnasari. "Hierarchical linear models on abdominal circumference research data using generalized least square." In THE 2016 CONFERENCE ON FUNDAMENTAL AND APPLIED SCIENCE FOR ADVANCED TECHNOLOGY (CONFAST 2016): Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research (ICPR 2016), International Conference on Industrial Biology (ICIBio 2016), and International Conference on Information System and Applied Mathematics (ICIAMath 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4953975.

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Le, Anh Tuyen, Yijiang Nan, Le Chung Tran, Xiaojing Huang, Y. Jay Guo, and Yiannis Vardaxoglou. "Analog Least Mean Square Loop for Self-Interference Cancellation in Generalized Continuous Wave SAR." In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE, 2018. http://dx.doi.org/10.1109/vtcfall.2018.8690717.

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Robberto, M. "A generalized least square algorithm to process infrared data taken in non-destructive readout mode." In SPIE Astronomical Telescopes + Instrumentation, edited by Jacobus M. Oschmann, Mark Clampin, Giovanni G. Fazio, and Howard A. MacEwen. SPIE, 2014. http://dx.doi.org/10.1117/12.2060114.

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Choi, Hon-Chit, Lingfeng Wen, Stefan Eberl, and Dagan Feng. "Fuzzy C-mean clustering on kinetic parameter estimation with generalized linear least square algorithm in SPECT." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.653017.

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Thompson, Lonny L., and Prapot Kunthong. "A Residual Based Variational Method for Reducing Dispersion Error in Finite Element Methods." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80551.

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A difficulty of the standard Galerkin finite element method has been the ability to accurately resolve oscillating wave solutions at higher frequencies. Many alternative methods have been developed including high-order methods, stabilized Galerkin methods, multi-scale variational methods, and other wave-based discretization methods. In this work, consistent residuals, both in the form of least-squares and gradient least-squares are linearly combined and added to the Galerkin variational Helmholtz equation to form a new generalized Galerkin least-squares method (GGLS). By allowing the stabilization parameters to vary spatially within each element, we are able to select optimal parameters which reduce dispersion error for all wave directions from second-order to fourth-order in nondimensional wavenumber; a substantial improvement over standard Galerkin elements. Furthermore, the stabilization parameters are frequency independent, and thus can be used for both time-harmonic solutions to the Helmholtz equation as well as direct time-integration of the wave equation, and eigenfrequency/eigenmodes analysis. Since the variational framework preserves consistency, high-order accuracy is maintained in the presence of source terms. In the case of homogeneous source terms, we show that our consistent variational framework is equivalent to integrating the underlying stiffness and mass matrices with optimally selected numerical quadrature rules. Optimal GGLS stabilization parameters and equivalent quadrature rules are determined for several element types including: bilinear quadrilateral, linear triangle, and linear tetrahedral elements. Numerical examples on unstructured meshes validate the expected high-order accuracy.
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Deng, Yi, Han Liu, Huilong Wang, and Ding Liu. "Implicit Generalized Predictive Control of multivariable systems based on online Least Square Support Vector Machines of inverse system." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6896901.

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Zhang, Jialiang, and Jianfu Cao. "Fault Diagnosis for Closed Loop Nonlinear System Using Generalized Frequency Response Functions and Least Square Support Vector Machine." In 2019 1st International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2019. http://dx.doi.org/10.1109/iciai.2019.8850734.

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