Relevant bibliographies by topics / Mixed data types

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  1. Journal articles

Academic literature on the topic 'Mixed data types'

Author: Grafiati
Published: 9 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "Mixed data types"

1

Wang, Lu, Dongxiao Zhu, and Ming Dong. "Clustering over-dispersed data with mixed feature types." Statistical Analysis and Data Mining: The ASA Data Science Journal 11, no. 2 (2018): 55–65. http://dx.doi.org/10.1002/sam.11369.

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2

Sánchez-Borrego, I., J. D. Opsomer, M. Rueda, and A. Arcos. "Nonparametric estimation with mixed data types in survey sampling." Revista Matemática Complutense 27, no. 2 (2013): 685–700. http://dx.doi.org/10.1007/s13163-013-0142-2.

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3

Jørgensen, Bent, Søren Lundbye-Christensen, Peter Xue-Kun Song, and Li Sun. "State-space models for multivariate longitudinal data of mixed types." Canadian Journal of Statistics 24, no. 3 (1996): 385–402. http://dx.doi.org/10.2307/3315747.

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4

Einarsson, Bo. "Mixed language programming realization and the provision of data types." ACM SIGNUM Newsletter 21, no. 1-2 (1986): 2–9. http://dx.doi.org/10.1145/15983.15984.

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5

Yoon, Grace, Raymond J. Carroll, and Irina Gaynanova. "Sparse semiparametric canonical correlation analysis for data of mixed types." Biometrika 107, no. 3 (2020): 609–25. http://dx.doi.org/10.1093/biomet/asaa007.

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Summary Canonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis based on the Gaussian copula. The main result of this paper is a truncated latent Gaussian copula model for data with excess zeros, which allows us to derive a rank-based estimator of the latent correlation matrix for mixed variable types without estimation of marginal transformation functions. The resulting canonical correlation analysis method works well in high-dimensional settings, as demonstrated via numerical studies, and when applied to the analysis of association between gene expression and microRNA data from breast cancer patients.
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6

Amatya, Anup, and Hakan Demirtas. "Concurrent generation of multivariate mixed data with variables of dissimilar types." Journal of Statistical Computation and Simulation 86, no. 18 (2016): 3595–607. http://dx.doi.org/10.1080/00949655.2016.1177530.

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7

Sun, Jinhui, Pang Du, Hongyu Miao, and Hua Liang. "Robust feature screening procedures for single and mixed types of data." Journal of Statistical Computation and Simulation 90, no. 7 (2020): 1173–93. http://dx.doi.org/10.1080/00949655.2020.1719104.

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8

Vogl, Susanne. "Integrating and Consolidating Data in Mixed Methods Data Analysis: Examples From Focus Group Data With Children." Journal of Mixed Methods Research 13, no. 4 (2018): 536–54. http://dx.doi.org/10.1177/1558689818796364.

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The challenge in data analysis often lies in accounting for the multidimensionality and complexity of the data while simultaneously discovering patterns. Integrating and consolidating different types of data during analysis can broaden the perspective and permit obtaining complementary views. This methodological research study on data collection illustrates how one type of data collection generates different types of data, which can be linked and consolidated to reach a better understanding of the topic. Procedures and practicalities are illustrated to offer a good practice example for data integration and consolidation. With the methodological reflection of research practice, I evaluate the consequences for the field of mixed methods research, in which the practicalities of an integrated mixed analysis still need to be elaborated.
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9

Harch, B. D., K. E. Basford, I. H. DeLacy, P. K. Lawrence, and A. Cruickshank. "Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data." Genetic Resources and Crop Evolution 43, no. 4 (1996): 363–76. http://dx.doi.org/10.1007/bf00132957.

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10

Huang, Mingze, Christian Müller, and Irina Gaynanova. "latentcor: An R Package for estimating latent correlations from mixed data types." Journal of Open Source Software 6, no. 65 (2021): 3634. http://dx.doi.org/10.21105/joss.03634.

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