Chien‐Pai Han

802 total citations
54 papers, 546 citations indexed

About

Chien‐Pai Han is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Chien‐Pai Han has authored 54 papers receiving a total of 546 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Statistics and Probability, 15 papers in Artificial Intelligence and 10 papers in Statistics, Probability and Uncertainty. Recurrent topics in Chien‐Pai Han's work include Advanced Statistical Methods and Models (22 papers), Bayesian Methods and Mixture Models (10 papers) and Statistical Methods and Inference (10 papers). Chien‐Pai Han is often cited by papers focused on Advanced Statistical Methods and Models (22 papers), Bayesian Methods and Mixture Models (10 papers) and Statistical Methods and Inference (10 papers). Chien‐Pai Han collaborates with scholars based in United States, South Korea and Canada. Chien‐Pai Han's co-authors include T. A. Bancroft, C. Venkata Rao, Ritu Gupta, D.L. Hawkins, Jianling Li, Iain D. Currie, Christopher Cox, Qizhai Li, Bo Li and Choudur Lakshminarayan and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and Biometrics.

In The Last Decade

Chien‐Pai Han

49 papers receiving 488 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chien‐Pai Han United States 10 334 86 81 48 30 54 546
Wing–Kam Fung Hong Kong 13 358 1.1× 47 0.5× 104 1.3× 50 1.0× 26 0.9× 28 482
Heinz Linhart South Africa 7 357 1.1× 186 2.2× 94 1.2× 80 1.7× 24 0.8× 22 777
Martin Bilodeau Canada 9 239 0.7× 78 0.9× 44 0.5× 42 0.9× 26 0.9× 26 441
Bo‐Cheng Wei China 16 583 1.7× 139 1.6× 78 1.0× 93 1.9× 28 0.9× 56 726
C. P. Quesenberry United States 13 378 1.1× 87 1.0× 218 2.7× 95 2.0× 14 0.5× 23 581
Dan Bradu United States 9 354 1.1× 62 0.7× 177 2.2× 56 1.2× 52 1.7× 18 687
Martin Schatzoff United States 10 347 1.0× 77 0.9× 132 1.6× 81 1.7× 60 2.0× 24 669
C. R. Rao China 5 129 0.4× 63 0.7× 28 0.3× 50 1.0× 18 0.6× 11 588
Louis A. Jaeckel United States 5 398 1.2× 62 0.7× 126 1.6× 47 1.0× 16 0.5× 8 510
C. M. Theobald United Kingdom 12 210 0.6× 47 0.5× 73 0.9× 27 0.6× 36 1.2× 29 488

Countries citing papers authored by Chien‐Pai Han

Since Specialization
Citations

This map shows the geographic impact of Chien‐Pai Han's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chien‐Pai Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chien‐Pai Han more than expected).

Fields of papers citing papers by Chien‐Pai Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chien‐Pai Han. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chien‐Pai Han. The network helps show where Chien‐Pai Han may publish in the future.

Co-authorship network of co-authors of Chien‐Pai Han

This figure shows the co-authorship network connecting the top 25 collaborators of Chien‐Pai Han. A scholar is included among the top collaborators of Chien‐Pai Han based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chien‐Pai Han. Chien‐Pai Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Qizhai, et al.. (2014). A hybrid approach for regression analysis with block missing data. Computational Statistics & Data Analysis. 75. 239–247. 3 indexed citations
2.
Han, Chien‐Pai, et al.. (2013). Conditional bootstrap confidence intervals for classification error rate when a block of observations is missing. Journal of the Korean Data and Information Science Society. 24(1). 189–200. 2 indexed citations
3.
Han, Chien‐Pai, et al.. (2009). Bootstrap confidence intervals for classification error rate in circular models when a block of observations is missing. Journal of the Korean Data and Information Science Society. 20(4). 757–764.
4.
Han, Chien‐Pai, et al.. (2002). The Combination Test for Multivariate Normality. Journal of Statistical Computation and Simulation. 72(5). 379–390. 6 indexed citations
5.
Hawkins, D.L. & Chien‐Pai Han. (2000). Estimating Transition Probabilities from Aggregate Samples Plus Partial Transition Data. Biometrics. 56(3). 848–854. 7 indexed citations
6.
Han, Chien‐Pai, et al.. (1995). Conditional interval estimation of the exponential location parameter following rejection of a pre-test. Communication in Statistics- Theory and Methods. 24(6). 1481–1492. 8 indexed citations
7.
Han, Chien‐Pai, et al.. (1995). Interval estimation of error variance following a preliminary test in one–way random model. Communications in Statistics - Simulation and Computation. 24(4). 817–824. 6 indexed citations
8.
Han, Chien‐Pai & D.L. Hawkins. (1994). A smooth adaptive estimator of the mean of a symmetric or asymmetric distribution. Communication in Statistics- Theory and Methods. 23(1). 1–10. 2 indexed citations
9.
Hawkins, D.L. & Chien‐Pai Han. (1986). A power comparison of three tests for design effects in a random effects covariance model. Communication in Statistics- Theory and Methods. 15(11). 3401–3418. 1 indexed citations
10.
Han, Chien‐Pai. (1985). NFD: Noncentral F Distribution. The American Statistician. 39(3). 211–211. 5 indexed citations
11.
Han, Chien‐Pai, et al.. (1983). Computation of noncentral f distributions with even denominator degrees of freedom. Communications in Statistics - Simulation and Computation. 12(1). 1–9. 2 indexed citations
12.
Bancroft, T. A. & Chien‐Pai Han. (1983). A Note on Pooling Variances. Journal of the American Statistical Association. 78(384). 981–983. 33 indexed citations
13.
Cox, Christopher & Chien‐Pai Han. (1982). Testing multivariate means when the covariance matrix has intraclass correlation structure. Journal of Statistical Computation and Simulation. 16(2). 97–107. 3 indexed citations
14.
Han, Chien‐Pai. (1978). On the computation of noncentral chi-squared distributions. Journal of Statistical Computation and Simulation. 6(3-4). 207–210. 6 indexed citations
15.
Han, Chien‐Pai & T. A. Bancroft. (1978). Estimating regression coefficients tinder conditional specification. Communication in Statistics- Theory and Methods. 7(1). 47–56.
16.
Bancroft, T. A., et al.. (1977). A Pooling Methodology for Regressions in Prediction. Biometrics. 33(1). 57–57. 7 indexed citations
17.
Bancroft, T. A., et al.. (1975). Power of Analysis of Variance Test Procedures for Incompletely Specified Fixed Models. The Annals of Statistics. 3(4). 6 indexed citations
18.
Han, Chien‐Pai. (1973). Double Sampling with Partial Information on Auxiliary Variables. Journal of the American Statistical Association. 68(344). 914–918. 4 indexed citations
19.
Han, Chien‐Pai. (1973). Double Sampling with Partial Information on Auxiliary Variables. Journal of the American Statistical Association. 68(344). 914–914. 2 indexed citations
20.
Han, Chien‐Pai. (1968). Testing the homogeneity of a set of correlated variances. Biometrika. 55(2). 317–326. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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