15 papers · ranked by Valyu relevance
Max Welz, Patrick Mair, Andreas Alfons
Polychoric correlation is often an important building block in the analysis of rating data, particularly for structural equation models. However, the commonly employed maximum likelihood (ML) estimator is highly susceptible to misspecification of the polychoric correlation model, for instance through violations of…
M Harshvardhan, Pritam Ranjan
Pearson correlation is the default measure of association in most statistical software, yet it is only appropriate for pairs of continuous variables with a linear relationship. When variables are binary, ordinal, or categorical, specialized methods (e.g., point-biserial, polychoric, tetrachoric, and Cramér's~$V$) may…
Pierre Martel, Francisco Mbofana, Simon Cousens
Background The DHS wealth index − based on a statistical technique known as principal component analysis − is used extensively in mainstream surveys and epidemiological studies to assign individuals to wealth categories from information collected on common assets and household characteristics. Since its development in…
Jarl K. Kampen, Arie Weeren
A simulation study was carried out to study the behaviour of the polychoric correlation coefficient in data not compliant with the assumption of underlying continuous variables. Such data can produce relatively high estimated polychoric correlations (in the order of .62). Applied researchers are prone to accept these…
Shaobo Jin, Fan Yang-Wallentin
Asymptotic robustness against misspecification of the underlying distribution for the polychoric correlation estimation is studied. The asymptotic normality of the pseudo-maximum likelihood estimator is derived using the two-step estimation procedure. The t distribution assumption and the skew-normal distribution…
Sharmistha Dey
| 1. | INTRODUCTION AND MOTIVATION | 2 | | --- | --- | --- | | 2. | LITERATURE SURVEY | 2 | | 3. | DESCRIPTION OF DATA | 3 | | | 3.1. Gross-Revenue | 5 | | | 3.2. Continuous Variables | 5 | | | 3.3. MPAA Ratings | 9 | | | 3.4. Sequel | 9 | | | 3.5. Genre | 11 | | 4. | SOFTWARE AND PACKAGES USED | 12 | | 5. | MODELING…
Nils Brandenburg
An essential step in exploratory factor analysis is to determine the optimal number of factors. The Next Eigenvalue Sufficiency Test (NEST; Achim, 2017) is a recent proposal to determine the number of factors based on significance tests of the statistical contributions of candidate factors indicated by eigenvalues of…
Dylan Clark‐Boucher, Jeffrey W. Miller
Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA are based on the Pearson correlation matrix, which is not well-suited to describing the relationships between non-Gaussian variables. We…
Peng Zhang, Ben Liu, Jingjing Pan
An iteratively reweighted least squares (IRLS) method is proposed for estimating polyserial and polychoric correlation coefficients in this paper. It iteratively calculates the slopes in a series of weighted linear regression models fitting on conditional expected values. For polyserial correlation coefficient…
Rudolf Debelak, Ulrich S. Tran, Mansour Ebrahimi
The analysis of polychoric correlations via principal component analysis and exploratory factor analysis are well-known approaches to determine the dimensionality of ordered categorical items. However, the application of these approaches has been considered as critical due to the possible indefiniteness of the…
Elena D. Stavrovskaya, Tejasvi Niranjan, Elana J. Fertig, Sarah J. Wheelan + 2 more
High throughput sequencing methods produce massive amounts of data. The most common first step in interpretation of these data is to map the data to genomic intervals and then overlap with genome annotations. A major interest in computational genomics is spatial genome-wide correlation among genomic features (e.g.…
Authors not listed
Sequence is the critical determinant of macromolecular function, yet current polymer design approaches often optimize monomer composition and ratios while ignoring sequence. This creates poorly defined design spaces for active learning that miss the vast combinatorial landscape of sequence possibilities. We introduce…
Manuela Royer-Carenzi, Gilles Didier
Being confounding factors, directional trends are likely to make two quantitative traits appear as spuriously correlated. By determining the probability distributions of independent contrasts when traits evolve following Brownian motions with linear trends, we show that the standard independent contrasts can not be…
Huwenbo Shi, Nicholas Mancuso, Sarah Spendlove, Bogdan Pasaniuc
Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions contribute to the genome-wide genetic correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation…
Edoardo Saccenti, Margriet H. W. B. Hendriks, Age K. Smilde
Correlation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates…