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About 10,000 papers for “component weights”

Weights of principal component indicators.

Jian-Lin Yuan (18469114), Nan Jing (5884445) · Figshare · 2024

<div><p>The importance of real estate development has been widely accepted by all countries. Through early warning and avoidance of real estate financial risks, it can effectively promote the healthy and healthy development of the real estate industry, avoiding the impact of accidental factors, such as the COVID-19 pandemic, and promoting the overall economic development. Based on multiple regression analysis and grey prediction methods, this article constructs a real estate financial risk estimation model, and the real estate financial risk is estimated using the relevant data of Liaoning Province from 2001 to 2020. Analyzing the research results of financial risks in Liaoning Province, we can find that the real estate financial risks reached the peak in 2013, and then the real estate financial risks gradually showed a slow decline trend. In general, the financial risks in Liaoning Province are controllable. The study of financial risks in Liaoning Province will help to judge the development of the real estate industry and promote the continuous improvement of the overall economy. The article, through the study of real estate financial risks in Liaoning Province, can promote the development of regional real estate in Liaoning Province and promote the overall economic development of Liaoning Province, which has strong practical significance. The study of real estate financial risks, relevant risk research theories can be enriched, the identification of financial risks can be improved, and the study of real estate financial risks can be strengthened. The article uses a combination of multivariate statistics and grey fuzzy theory to complete the study of real estate financial risks. Therefore, through the exploration of multivariate statistics and grey fuzzy theory, its application value can be elevated.</p></div>

Cited by 0Open access

Principal component analysis of face weights.

Martin Wegrzyn (417215), Maria Vogt (4020335), Berna Kireclioglu (4020338) et al. · Figshare · 2017

<p>a, percentage of explained variance by the first five principal components; b, visualisation of weights of each principal component in “tile space”, with positive weights in red and negative weights in blue; c, plotting each face in the space defined by the first two principal components, error bars represent 95% confidence intervals; d, same as c, but with the actual stimuli replacing the markers.</p>

Cited by 0Open access

A component GARCH model with time varying weights

Giuseppe Storti, Luc Bauwens · RePEc: Research Papers in Economics

The empirical evidence from financial markets suggests that the pattern of response of market volatility to shocks is highly dependent on the magnitude of shocks themselves. Markov-Switching GARCH (MS-GARCH) models are a valuable tool for modelling state dependence in the dynamics of the volatility process. However, their application is still limited by the severe difficulties arising at the estimation and identification stages. In order to allow for time varying persistence in the volatility dynamics, it is here suggested to use a modification of the component GARCH model proposed by Ding and Granger (1996) in which the weights associated to the model components are time varying and depend on adequately chosen state variables such as lagged values of the conditional standard deviation. Differently from MS-GARCH models, likelihood based inference for the proposed model is readily available using standard numerical tools. Since the proposed model implies a non-linear representation for the squared observations, the generation of multi-step-ahead volatility predictions imposes some additional difficulties with respect to standard GARCH models, for which a linear ARMA representation can be obtained. In the paper, we apply simulation based techniques for estimating the predictive density of returns.

Cited by 0

Heatmap visualization of the H matrix component weights.

Laura M. Gunsalus (9234497), Michael J. Keiser (260659), Katherine S. Pollard (8685789) · Figshare · 2025

We selected a balanced subset of 500 transcribing and 500 non-transcribing cells with the highest component contributions. Rows (components) and columns (cells) were hierarchically clustered. Cells are labeled by transcriptional state (transcribing/non-transcribing), revealing patterns of component weights that correlate with transcriptional activity. (TIFF)

Cited by 0Open access

Keywords：Principal Component Analysis Coal blend Coke Weights Evaluate

Chen Hong-jun, Bai Jin-feng · 2016

Abstract：In order to make an accurate prediction of coke quality, which indicators need to find out with the coal for coke affect. This paper proposes a principal component analysis to determine with the indicators of coal coke weight, the results show that this method can effectively confirm with the main component of coal coke impact, and lay a solid foundation for subsequent coke prediction.

Cited by 0

Weights of variables in the LDA component for MGS.

Emanuela Dalla Costa (539674), Riccardo Pascuzzo (4588732), Matthew C. Leach (308464) et al. · Figshare · 2018

<p>Weight for each FAUs of the MGS that best discriminate between the categories (“no pain status” vs “pain status”).</p>

Cited by 0Open access

Weights of variables in the LDA component for HGS.

Emanuela Dalla Costa (539674), Riccardo Pascuzzo (4588732), Matthew C. Leach (308464) et al. · Figshare · 2018

<p>Weights for each FAUs of HGS that best discriminate between the “Pain” and “No Pain” groups.</p>

Cited by 0Open access

Age-specific estimated probabilities (weights) of the component distributions.

Dennis te Beest (663177), Erwin de Bruin (276936), Sandra Imholz (149275) et al. · Figshare · 2015

<p>Three scenarios are considered, viz. a univariate model that uses A/2009 (H1N1) microarray data (‘Univariate microarray’), a bivariate model that uses A/2009 (H1N1) and A/1918 (H1N1) microarray data (‘Bivariate microarray’), and a univariate model of hemagglutination inhibition data using A/2009 (H1N1) (‘Univariate HI’). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113021#pone.0113021.s002" target="_blank">Table S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113021#pone.0113021.s003" target="_blank">Table S3</a> for estimates of the parameters of the distributions.</p>a)<p>one-sided confidence interval.</p><p>Age-specific estimated probabilities (weights) of the component distributions.</p>

Cited by 0Open access

Selection of candidate cell assemblies with same-sign component weights.

Marco N. Pompili (12288422), Ralitsa Todorova (12243945), Céline J. Boucly (22794494) et al. · Figshare · 2025

<b>(a)</b> Cell assembly weights of three representative prefrontal assemblies (colored circles: assembly members, black circles: nonmembers), corresponding to eigenvalues 1, 28, and 38. Whereas all members of assemblies 1 and 28 were of the same (positive) sign, assembly 38 included members with both positive and negative weights (“mixed-sign assembly”). <b>(b)</b> The separation between members and nonmembers was significantly better in same-sign assemblies than mixed-sign assemblies (***<i>p</i> < 0.001, Wilcoxon rank sum test). <b>(c)</b> Mixed-sign assemblies had significantly more members than same-sign assemblies (***<i>p</i> < 0.001, Wilcoxon rank sum test). The data underlying this Figure can be found in <a href="https://doi.org/10.6080/K09W0CQP" target="_blank">https://doi.org/10.6080/K09W0CQP</a>. (PDF)

Cited by 0Open access

Predicting body weights of Kolhapuri sheep using principal component scores

Dinesh Kumar Yadav, Reena Arora, Anand Jain · Indian Journal of Small Ruminants (The) · 2021

This study, in addition to defining the morphometric structure of Kolhapuri sheep, predicts body weights using the principal component analysis (PCA) of the morphometric traits. Data on body weights and 13 body measurements were collected on 265 Kolhapuri sheep in the breeding tract. For the traits measured, the coefficients of variation (%) ranged from 5.1 (rump width) to 18.8 (body weight). In general, the phenotypic correlations between body weights and morphometric traits were positive and significant (p<0.05/0.01). Very high correlation (0.96) between withers height and rump height suggested colinearity between the two traits. Two components were derived from the PCA of morphometric traits with the total variance explained in the data by 67.4%. Using morphometric variables as predictors, regression models explained up to 85% of the body weight variance, while up to 86% was explained by using principal components as predictors. The multiple linear regression analysis suggested that use of the principal component scores was better than the use of original correlated traits in predicting the body weights of sheep.

Cited by 1Open access

Fast component-by-component construction of lattice algorithms for multivariate approximation with POD and SPOD weights

Ronald Cools, Frances Y. Kuo, Dirk Nuyens et al. · Mathematics of Computation · 2020

In a recent paper by the same authors, we provided a theoretical foundation for the component-by-component (CBC) construction of lattice algorithms for multivariate $L_2$ approximation in the worst case setting, for functions in a periodic space with general weight parameters. The construction led to an error bound that achieves the best possible rate of convergence for lattice algorithms. Previously available literature covered only weights of a simple form commonly known as

Cited by 1Open access