Search results for "least square"
showing 10 items of 286 documents
Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research : A commentary on Yuan and Fang (2023)
2023
In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller stan…
Quantile regression via iterative least squares computations
2012
We present an estimating framework for quantile regression where the usual L 1-norm objective function is replaced by its smooth parametric approximation. An exact path-following algorithm is derived, leading to the well-known ‘basic’ solutions interpolating exactly a number of observations equal to the number of parameters being estimated. We discuss briefly possible practical implications of the proposed approach, such as early stopping for large data sets, confidence intervals, and additional topics for future research.
Electricity consumption prediction with functional linear regression using spline estimators
2010
A functional linear regression model linking observations of a functional response variable with measurements of an explanatory functional variable is considered. This model serves to analyse a real data set describing electricity consumption in Sardinia. The interest lies in predicting either oncoming weekends’ or oncoming weekdays’ consumption, provided actual weekdays’ consumption is known. A B-spline estimator of the functional parameter is used. Selected computational issues are addressed as well.
How do brand personality, identification, and relationship length drive loyalty in sports?
2016
Purpose – The purpose of this paper is to extend brand identification theory to the sports team context by testing the direct and indirect effects of a sports team’s personality, sports fans’ identification with the team, and the effect of the length of fans’ relationship with a team on their loyalty to it. Design/methodology/approach – The authors conducted a quantitative study among ice hockey fans of one Finnish hockey team before play-off games. Data came from an online questionnaire generating 1,166 responses. Findings – The authors find that: first, identification with a team mediates the effects of brand personality on attitudinal loyalty and behavioral loyalty; second, brand person…
Partial least squares modelization of energy dispersive X-ray fluorescence.
2019
As a proof of concept, a green methodology has been developed for the energy dispersive X-ray fluorescence (ED-XRF) determination of calcium, potassium, iron, magnesium, aluminum, chromium, strontium, phosphorus and nickel in the peel of untreated kaki fruit (Diospyros kaki. L) samples. ED-XRF spectra of fifty-six kakis purchased in the local area of LLombay (Valencia) were obtained directly from samples without any previous treatment and without sample damage just after cleaning the fruit with distilled water. Inductively Couple Plasma Optical Emission Spectrometry (ICP-OES) was used as a reference method to determine the mineral elements after microwave assisted acid digestion. XRF spectr…
Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos
2015
[EN] Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were…
Assessing the territorial influence of an Iberian worship site. The chemical characterisation of the terracotta from the Iron Age sanctuary of La Ser…
2017
This paper presents the study of the prestigious terracotta votive figurines from the Iberian Iron Age sanctuary of La Serreta (Alicante province, Spain) composed of 174 items. Portable X-ray fluorescence (PXRF) was used to identify elemental markers that permit us to observe the differences between local and non-local terracotta figurines and furthermore to evaluate the geographical influence of the La Serreta sanctuary using Principal Component Analysis (PCA). The Partial Least Squares Discriminant Analysis (PLSDA) statistical method was also used to classify the figurines of uncertain geographical origin. The resulting groups were related to typological and stylistic groups of figurines …
A rapid method for the differentiation of yeast cells grown under carbon and nitrogen-limited conditions by means of partial least squares discrimina…
2012
This paper shows the ease of application and usefulness of mid-IR measurements for the investigation of orthogonal cell states on the example of the analysis of Pichia pastoris cells. A rapid method for the discrimination of entire yeast cells grown under carbon and nitrogen-limited conditions based on the direct acquisition of mid-IR spectra and partial least squares discriminant analysis (PLS-DA) is described. The obtained PLS-DA model was extensively validated employing two different validation strategies: (i) statistical validation employing a method based on permutation testing and (ii) external validation splitting the available data into two independent sub-sets. The Variable Importa…
Prediction of organic carbon and total nitrogen contents in organic wastes and their composts by Infrared spectroscopy and partial least square regre…
2017
Middle and near infrared (MIR and NIR) were employed to determine organic carbon (OC) and total nitrogen (TN) in different soil organic amendments including wastes, composts and mixtures of composts and organic wastes. Prediction models based on partial least squares (PLS) regression from the spectra of untreated samples were built. Different spectra preprocessing strategies were adopted and the best number of latent variable was evaluated using leave-one-out cross-validation. Attenuated total reflectance (PLS-ATR-MIR) and diffuse reflectance (PLS-DR-NIR) models were built and evaluated from root mean square error of cross validation and prediction (RMSECV and RMSEP), coefficients of determ…