Relevant bibliographies by topics / Penalized least squares

Contents

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

Academic literature on the topic 'Penalized least squares'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Penalized least squares"

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Kaufman, L. "Maximum likelihood, least squares, and penalized least squares for PET." IEEE Transactions on Medical Imaging 12, no. 2 (June 1993): 200–214. http://dx.doi.org/10.1109/42.232249.

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Eubank, R. L., and R. F. Gunst. "Diagnostics for penalized least-squares estimators." Statistics & Probability Letters 4, no. 5 (August 1986): 265–72. http://dx.doi.org/10.1016/0167-7152(86)90101-x.

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Wibowo, Wahyu, Sri Haryatmi, and I. Nyoman Budiantara. "Penalized least squares for semiparametric regression." International Journal of Academic Research 4, no. 6 (November 9, 2012): 281–86. http://dx.doi.org/10.7813/2075-4124.2012/4-6/a.39.

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Kohler, M., and A. Krzyzak. "Nonparametric regression estimation using penalized least squares." IEEE Transactions on Information Theory 47, no. 7 (2001): 3054–58. http://dx.doi.org/10.1109/18.998089.

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Bates, Douglas M., and Saikat DebRoy. "Linear mixed models and penalized least squares." Journal of Multivariate Analysis 91, no. 1 (October 2004): 1–17. http://dx.doi.org/10.1016/j.jmva.2004.04.013.

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Guerrero, Victor M. "Time series smoothing by penalized least squares." Statistics & Probability Letters 77, no. 12 (July 2007): 1225–34. http://dx.doi.org/10.1016/j.spl.2007.03.006.

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Peng, Heng, and Tao Huang. "Penalized least squares for single index models." Journal of Statistical Planning and Inference 141, no. 4 (April 2011): 1362–79. http://dx.doi.org/10.1016/j.jspi.2010.10.003.

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Wittich, O., A. Kempe, G. Winkler, and V. Liebscher. "Complexity penalized least squares estimators: Analytical results." Mathematische Nachrichten 281, no. 4 (April 2008): 582–95. http://dx.doi.org/10.1002/mana.200510627.

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Spiriti, Steven, Randall Eubank, Philip W. Smith, and Dennis Young. "Knot selection for least-squares and penalized splines." Journal of Statistical Computation and Simulation 83, no. 6 (June 2013): 1020–36. http://dx.doi.org/10.1080/00949655.2011.647317.

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Zhu, Rong, Guohua Zou, Hua Liang, and Lixing Zhu. "Penalized Weighted Least Squares to Small Area Estimation." Scandinavian Journal of Statistics 43, no. 3 (December 18, 2015): 736–56. http://dx.doi.org/10.1111/sjos.12201.

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Dissertations / Theses on the topic "Penalized least squares"

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Munoz, Maldonado Yolanda. "Mixed models, posterior means and penalized least squares." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2637.

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In recent years there has been increased research activity in the area of Func- tional Data Analysis. Methodology from finite dimensional multivariate analysis has been extended to the functional data setting giving birth to Functional ANOVA, Functional Principal Components Analysis, etc. In particular, some studies have pro- posed inferential techniques for various functional models that have connections to well known areas such as mixed-effects models or spline smoothing. The methodol- ogy used in these cases is computationally intensive since it involves the estimation of coefficients in linear models, adaptive selection of smoothing parameters, estimation of variances components, etc. This dissertation proposes a wide-ranging modeling framework that includes many functional linear models as special cases. Three widely used tools are con- sidered: mixed-effects models, penalized least squares, and Bayesian prediction. We show that, in certain important cases, the same numerical answer is obtained for these seemingly different techniques. In addition, under certain assumptions, an applica- tion of a Kalman filter algorithm is shown to improve the order of computations, by two orders of magnitude, for point and interval estimates (with n being the sample size). A functional data analysis setting is used to exemplify our results.
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Pechmann, Patrick R. "Penalized Least Squares Methoden mit stückweise polynomialen Funktionen zur Lösung von partiellen Differentialgleichungen." kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2008/2813/.

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Garreau, Damien. "Change-point detection and kernel methods." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE061/document.

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Dans cette thèse, nous nous intéressons à une méthode de détection des ruptures dans une suite d’observations appartenant à un ensemble muni d’un noyau semi-défini positif. Cette procédure est une version « à noyaux » d’une méthode des moindres carrés pénalisés. Notre principale contribution est de montrer que, pour tout noyau satisfaisant des hypothèses raisonnables, cette méthode fournit une segmentation proche de la véritable segmentation avec grande probabilité. Ce résultat est obtenu pour un noyau borné et une pénalité linéaire, ainsi qu’une autre pénalité venant de la sélection de modèles. Les preuves reposent sur un résultat de concentration pour des variables aléatoires bornées à valeurs dans un espace de Hilbert, et nous obtenons une version moins précise de ce résultat lorsque l’on supposeseulement que la variance des observations est finie. Dans un cadre asymptotique, nous retrouvons les taux minimax usuels en détection de ruptures lorsqu’aucune hypothèse n’est faite sur la taille des segments. Ces résultats théoriques sont confirmés par des simulations. Nous étudions également de manière détaillée les liens entre différentes notions de distances entre segmentations. En particulier, nous prouvons que toutes ces notions coïncident pour des segmentations suffisamment proches. D’un point de vue pratique, nous montrons que l’heuristique du « saut de dimension » pour choisir la constante de pénalisation est un choix raisonnable lorsque celle-ci est linéaire. Nous montrons également qu’une quantité clé dépendant du noyau et qui apparaît dans nos résultats théoriques influe sur les performances de cette méthode pour la détection d’une unique rupture. Dans un cadre paramétrique, et lorsque le noyau utilisé est invariant partranslation, il est possible de calculer cette quantité explicitement. Grâce à ces calculs, nouveaux pour plusieurs d’entre eux, nous sommes capable d’étudier précisément le comportement de la constante de pénalité maximale. Pour finir, nous traitons de l’heuristique de la médiane, un moyen courant de choisir la largeur de bande des noyaux à base de fonctions radiales. Dans un cadre asymptotique, nous montrons que l’heuristique de la médiane se comporte à la limite comme la médiane d’une distribution que nous décrivons complètement dans le cadre du test à deux échantillons à noyaux et de la détection de ruptures. Plus précisément, nous montrons que l’heuristique de la médiane est approximativement normale centrée en cette valeur
In this thesis, we focus on a method for detecting abrupt changes in a sequence of independent observations belonging to an arbitrary set on which a positive semidefinite kernel is defined. That method, kernel changepoint detection, is a kernelized version of a penalized least-squares procedure. Our main contribution is to show that, for any kernel satisfying some reasonably mild hypotheses, this procedure outputs a segmentation close to the true segmentation with high probability. This result is obtained under a bounded assumption on the kernel for a linear penalty and for another penalty function, coming from model selection.The proofs rely on a concentration result for bounded random variables in Hilbert spaces and we prove a less powerful result under relaxed hypotheses—a finite variance assumption. In the asymptotic setting, we show that we recover the minimax rate for the change-point locations without additional hypothesis on the segment sizes. We provide empirical evidence supporting these claims. Another contribution of this thesis is the detailed presentation of the different notions of distances between segmentations. Additionally, we prove a result showing these different notions coincide for sufficiently close segmentations.From a practical point of view, we demonstrate how the so-called dimension jump heuristic can be a reasonable choice of penalty constant when using kernel changepoint detection with a linear penalty. We also show how a key quantity depending on the kernelthat appears in our theoretical results influences the performance of kernel change-point detection in the case of a single change-point. When the kernel is translationinvariant and parametric assumptions are made, it is possible to compute this quantity in closed-form. Thanks to these computations, some of them novel, we are able to study precisely the behavior of the maximal penalty constant. Finally, we study the median heuristic, a popular tool to set the bandwidth of radial basis function kernels. Fora large sample size, we show that it behaves approximately as the median of a distribution that we describe completely in the setting of kernel two-sample test and kernel change-point detection. More precisely, we show that the median heuristic is asymptotically normal around this value
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Krčál, Adam. "High-dimensional VAR analysis of regional house prices in United States." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-202128.

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In this thesis the heterogeneity of regional real estate prices in United States is investigated. A high dimensional VAR model with additional exogenous predictors, originally introduced by \cite{fan11}, is adopted. In this framework, the common factor in regional house prices dynamics is explained by exogenous predictors and the spatial dependencies are captured by lagged house prices in other regions. For the purpose of estimation and variable selection under high-dimensional setting the concept of Penalized Least Squares (PLS) with different penalty functions (e.g. LASSO penalty) is studied in detail and implemented. Moreover, clustering methods are employed to identify subsets of statistical regions with similar house prices dynamics. It is demonstrated that these clusters are well geographically defined and contribute to a better interpretation of the VAR model. Next, we make use of the LASSO variable selection property in order to construct the impulse response functions and to simulate the prices behavior when a shock occurs. And last but not least, one-period-ahead forecasts from VAR model are compared to those from the Diffusion Index Factor Model by \cite{stock02}, a commonly used model for forecasts.
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Sorba, Olivier. "Pénalités minimales pour la sélection de modèle." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS043/document.

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Dans le cadre de la sélection de modèle par contraste pénalisé, L. Birgé and P. Massart ont prouvé que le phénomène de pénalité minimale se produit pour la sélection libre parmi des variables gaussiennes indépendantes. Nous étendons certains de leurs résultats à la partition d'un signal gaussien lorsque la famille de partitions envisagées est suffisamment riche, notamment dans le cas des arbres de régression. Nous montrons que le même phénomène se produit dans le cadre de l'estimation de densité. La richesse de la famille de modèle s'apparente à une forme d'isotropie. De ce point de vue le phénomène de pénalité minimale est intrinsèque. Pour corroborer et illustrer ce point de vue, nous montrons que le même phénomène se produit pour une famille de modèles d'orientation aléatoire uniforme
L. Birgé and P. Massart proved that the minimum penalty phenomenon occurs in Gaussian model selection when the model family arises from complete variable selection among independent variables. We extend some of their results to discrete Gaussian signal segmentation when the model family corresponds to a sufficiently rich family of partitions of the signal's support. This is the case of regression trees. We show that the same phenomenon occurs in the context of density estimation. The richness of the model family can be related to a certain form of isotropy. In this respect the minimum penalty phenomenon is intrinsic. To corroborate this point of view, we show that the minimum penalty phenomenon occurs when the models are chosen randomly under an isotropic law
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Chen, Long. "Méthodes itératives de reconstruction tomographique pour la réduction des artefacts métalliques et de la dose en imagerie dentaire." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112015/document.

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Cette thèse est constituée de deux principaux axes de recherche portant sur l'imagerie dentaire par la tomographie à rayons X : le développement de nouvelles méthodes itératives de reconstruction tomographique afin de réduire les artefacts métalliques et la réduction de la dose délivrée au patient. Afin de réduire les artefacts métalliques, nous prendrons en compte le durcissement du spectre des faisceaux de rayons X et le rayonnement diffusé. La réduction de la dose est abordée dans cette thèse en diminuant le nombre des projections traitées. La tomographie par rayons X a pour objectif de reconstruire la cartographie des coefficients d'atténuations d'un objet inconnu de façon non destructive. Les bases mathématiques de la tomographie repose sur la transformée de Radon et son inversion. Néanmoins des artefacts métalliques apparaissent dans les images reconstruites en inversant la transformée de Radon (la méthode de rétro-projection filtrée), un certain nombre d'hypothèse faites dans cette approche ne sont pas vérifiées. En effet, la présence de métaux exacerbe les phénomènes de durcissement de spectre et l'absence de prise en compte du rayonnement diffusé. Nous nous intéressons dans cette thèse aux méthodes itératives issues d'une méthodologie Bayésienne. Afin d'obtenir des résultats de traitement compatible avec une application clinique de nos nouvelles approches, nous avons choisi un modèle direct relativement simple et classique (linéaire) associé à des approches de corrections de données. De plus, nous avons pris en compte l'incertitude liée à la correction des données en utilisant la minimisation d'un critère de moindres carrés pondérés. Nous proposons donc une nouvelle méthode de correction du durcissement du métal sans connaissances du spectre de la source et des coefficients d'atténuation des matériaux. Nous proposons également une nouvelle méthode de correction du diffusé associée sur les mesures sous certaines conditions notamment de faible dose. En imagerie médicale par tomographie à rayons X, la surexposition ou exposition non nécessaire irradiante augmente le risque de cancer radio-induit lors d'un examen du patient. Notre deuxième axe de recherche porte donc sur la réduction de la dose en diminuant le nombre de projections. Nous avons donc introduit un nouveau mode d'acquisition possédant un échantillonnage angulaire adaptatif. On utilise pour définir cette acquisition notre connaissance a priori de l'objet. Ce mode d'acquisition associé à un algorithme de reconstruction dédié, nous permet de réduire le nombre de projections tout en obtenant une qualité de reconstruction comparable au mode d'acquisition classique. Enfin, dans certains modes d’acquisition des scanners dentaires, nous avons un détecteur qui n'arrive pas à couvrir l'ensemble de l'objet. Pour s'affranchir aux problèmes liés à la tomographie locale qui se pose alors, nous utilisons des acquisitions multiples suivant des trajectoires circulaires. Nous avons adaptés les résultats développés par l’approche « super short scan » [Noo et al 2003] à cette trajectoire très particulière et au fait que le détecteur mesure uniquement des projections tronquées. Nous avons évalué nos méthodes de réduction des artefacts métalliques et de réduction de la dose en diminuant le nombre des projections sur les données réelles. Grâce à nos méthodes de réduction des artefacts métalliques, l'amélioration de qualité des images est indéniable et il n'y a pas d'introduction de nouveaux artefacts en comparant avec la méthode de l'état de l'art NMAR [Meyer et al 2010]. Par ailleurs, nous avons réussi à réduire le nombre des projections avec notre nouveau mode d'acquisition basé sur un « super short scan » appliqué à des trajectoires multiples. La qualité obtenue est comparable aux reconstructions obtenues avec les modes d'acquisition classiques ou short-scan mais avec une réduction d’au moins 20% de la dose radioactive
This thesis contains two main themes: development of new iterative approaches for metal artifact reduction (MAR) and dose reduction in dental CT (Computed Tomography). The metal artifacts are mainly due to the beam-hardening, scatter and photon starvation in case of metal in contrast background like metallic dental implants in teeth. The first issue concerns about data correction on account of these effects. The second one involves the radiation dose reduction delivered to a patient by decreasing the number of projections. At first, the polychromatic spectra of X-ray beam and scatter can be modeled by a non-linear direct modeling in the statistical methods for the purpose of the metal artifacts reduction. However, the reconstruction by statistical methods is too much time consuming. Consequently, we proposed an iterative algorithm with a linear direct modeling based on data correction (beam-hardening and scatter). We introduced a new beam-hardening correction without knowledge of the spectra of X-ray source and the linear attenuation coefficients of the materials and a new scatter estimation method based on the measurements as well. Later, we continued to study the iterative approaches of dose reduction since the over-exposition or unnecessary exposition of irradiation during a CT scan has been increasing the patient's risk of radio-induced cancer. In practice, it may be useful that one can reconstruct an object larger than the field of view of scanner. We proposed an iterative algorithm on super-short-scans on multiple scans in this case, which contain a minimal set of the projections for an optimal dose. Furthermore, we introduced a new scanning mode of variant angular sampling to reduce the number of projections on a single scan. This was adapted to the properties and predefined interesting regions of the scanned object. It needed fewer projections than the standard scanning mode of uniform angular sampling to reconstruct the objet. All of our approaches for MAR and dose reduction have been evaluated on real data. Thanks to our MAR methods, the quality of reconstructed images was improved noticeably. Besides, it did not introduce some new artifacts compared to the MAR method of state of art NMAR [Meyer et al 2010]. We could reduce obviously the number of projections with the proposed new scanning mode and schema of super-short-scans on multiple scans in particular case
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Pechmann, Patrick R. "Penalized Least Squares Methoden mit stückweise polynomialen Funktionen zur Lösung von partiellen Differentialgleichungen." Doctoral thesis, 2008. https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-28136.

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Das Hauptgebiet der Arbeit stellt die Approximation der Lösungen partieller Differentialgleichungen mit Dirichlet-Randbedingungen durch Splinefunktionen dar. Partielle Differentialgleichungen finden ihre Anwendung beispielsweise in Bereichen der Elektrostatik, der Elastizitätstheorie, der Strömungslehre sowie bei der Untersuchung der Ausbreitung von Wärme und Schall. Manche Approximationsaufgaben besitzen keine eindeutige Lösung. Durch Anwendung der Penalized Least Squares Methode wurde gezeigt, dass die Eindeutigkeit der gesuchten Lösung von gewissen Minimierungsaufgaben sichergestellt werden kann. Unter Umständen lässt sich sogar eine höhere Stabilität des numerischen Verfahrens gewinnen. Für die numerischen Betrachtungen wurde ein umfangreiches, effizientes C-Programm erstellt, welches die Grundlage zur Bestätigung der theoretischen Voraussagen mit den praktischen Anwendungen bildete
This work focuses on approximating solutions of partial differential equations with Dirichlet boundary conditions by means of spline functions. The application of partial differential equations concerns the fields of electrostatics, elasticity, fluid flow as well as the analysis of the propagation of heat and sound. Some approximation problems do not have a unique solution. By applying the penalized least squares method it has been shown that uniqueness of the solution of a certain class of minimizing problems can be guaranteed. In some cases it is even possible to reach higher stability of the numerical method. For the numerical analysis we have developed an extensive and efficient C code. It serves as the basis to confirm theoretical predictions with practical applications
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Pechmann, Patrick R. [Verfasser]. "Penalized least squares Methoden mit stückweise polynomialen Funktionen zur Lösung von partiellen Differentialgleichungen / vorgelegt von Patrick R. Pechmann." 2008. http://d-nb.info/989690660/34.

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Chlubnová, Tereza. "Výběr modelu na základě penalizované věrohodnosti." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-347986.

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Selection of variables and estimation of regression coefficients in datasets with the number of variables exceeding the number of observations consti- tutes an often discussed topic in modern statistics. Today the maximum penalized likelihood method with an appropriately selected function of the parameter as the penalty is used for solving this problem. The penalty should evaluate the benefit of the variable and possibly mitigate or nullify the re- spective regression coefficient. The SCAD and LASSO penalty functions are popular for their ability to choose appropriate regressors and at the same time estimate the parameters in a model. This thesis presents an overview of up to date results in the area of characteristics of estimates obtained by using these two methods for both small number of regressors and multidimensional datasets in a normal linear model. Due to the fact that the amount of pe- nalty and therefore also the choice of the model is heavily influenced by the tuning parameter, this thesis further discusses its selection. The behavior of the LASSO and SCAD penalty functions for different values and possibili- ties for selection of the tuning parameter is tested with various numbers of regressors on simulated datasets.
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Mansor, Mohd Mahayaudin bin. "Directionality in time series and its applications." Thesis, 2017. http://hdl.handle.net/2440/114245.

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A suite of seven statistics to detect directionality in time series is presented. Applications from various disciplines including business, environmental science, finance and medicine are considered. Models that allow for directionality are proposed, and methods of fitting these models are investigated. Time series models that incorporate directionality provide more precise prediction limits and more realistic simulations than the models that do not. Potential practical applications include: providing evidence to support physical interpretations; directionality trading rules for investment portfolio; prediction of unstable financial periods; and possible early warning of epileptic seizures.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Mathematical Sciences, 2018
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Books on the topic "Penalized least squares"

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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 "Penalized least squares"

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Von Golitschek, M., and L. L. Schumaker. "Data fitting by penalized least squares." In Algorithms for Approximation II, 210–27. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-3442-0_20.

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Bunea, Florentina, Alexandre B. Tsybakov, and Marten H. Wegkamp. "Aggregation and Sparsity Via ℓ1 Penalized Least Squares." In Learning Theory, 379–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_29.

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Letchford, Adrian, Junbin Gao, and Lihong Zheng. "Penalized Least Squares for Smoothing Financial Time Series." In AI 2011: Advances in Artificial Intelligence, 72–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25832-9_8.

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Muñoz Maldonado, Yolanda. "Mixed Models, Posterior Means and Penalized Least-Squares." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 216–36. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2009. http://dx.doi.org/10.1214/09-lnms5713.

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Beran, Rudolf. "Multivariate regression through affinely weighted penalized least squares." In Institute of Mathematical Statistics Collections, 33–46. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2013. http://dx.doi.org/10.1214/12-imscoll904.

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Huang, Jian, and Huiliang Xie. "Asymptotic oracle properties of SCAD-penalized least squares estimators." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 149–66. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000337.

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Moulart, Raphaël, and René Rotinat. "Evaluation of the Penalized Least Squares Method for Strain Computation." In Conference Proceedings of the Society for Experimental Mechanics Series, 43–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22449-7_5.

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Ma, Zongjie, Huawen Liu, Kaile Su, and Zhonglong Zheng. "PPML: Penalized Partial Least Squares Discriminant Analysis for Multi-Label Learning." In Web-Age Information Management, 645–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08010-9_69.

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Schaffrin, Burkhard. "On Penalized Least-Squares: Its Mean Squared Error and a Quasi-Optimal Weight Ratio." In Recent Advances in Linear Models and Related Areas, 313–22. Heidelberg: Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2064-5_16.

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Sawatzky, Alex. "Performance of First-Order Algorithms for TV Penalized Weighted Least-Squares Denoising Problem." In Lecture Notes in Computer Science, 340–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07998-1_39.

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Conference papers on the topic "Penalized least squares"

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Gudmundson, Erik, and Petre Stoica. "On denoising via penalized least-squares rules." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518457.

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Liu, Huawen, Zongjie Ma, Jianmin Zhao, and Zhonglong Zheng. "Penalized partial least squares for multi-label data." In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921635.

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Li, Yuqiang, Tianhong Pan, Haoran Li, and Shan Chen. "Baseline correction using local smoothing optimization penalized least squares." In 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP). IEEE, 2022. http://dx.doi.org/10.1109/adconip55568.2022.9894165.

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Panahi, Ashkan, and Mats Viberg. "A novel method of DOA tracking by penalized least squares." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714007.

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Iatrou, Maria, Bruno De Man, Kedar Khare, and Thomas M. Benson. "A 3D study comparing filtered backprojection, weighted least squares, and penalized weighted least squares for CT reconstruction." In 2007 IEEE Nuclear Science Symposium Conference Record. IEEE, 2007. http://dx.doi.org/10.1109/nssmic.2007.4436689.

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Iatrou, M., B. De Man, and S. Basu. "A comparison between Filtered Backprojection, Post-Smoothed Weighted Least Squares, and Penalized Weighted Least Squares for CT reconstruction." In 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.356470.

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Gao, Ning, and Cai-yun Gao. "The Application of Penalized Least Squares Estimation to GPS Height Fitting." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5365251.

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Kumar, B. Naveen, and S. Chris Prema. "Noise variance estimation through penalized least-squares for ED-spectrum sensing." In 2016 International Conference on Communication Systems and Networks (ComNet). IEEE, 2016. http://dx.doi.org/10.1109/csn.2016.7823980.

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Treister, Eran, and Irad Yavneh. "A multilevel iterated-shrinkage approach to l-1 penalized least-squares minimization." In 2012 IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI 2012). IEEE, 2012. http://dx.doi.org/10.1109/eeei.2012.6377004.

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Wang, Jing, Tianfang Li, Hongbing Lu, and Zhengrong Liang. "Penalized weighted least-squares approach for low-dose x-ray computed tomography." In Medical Imaging, edited by Michael J. Flynn and Jiang Hsieh. SPIE, 2006. http://dx.doi.org/10.1117/12.653903.

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