Nan‐Jung Hsu

693 total citations
28 papers, 441 citations indexed

About

Nan‐Jung Hsu is a scholar working on Economics and Econometrics, Statistics and Probability and Environmental Engineering. According to data from OpenAlex, Nan‐Jung Hsu has authored 28 papers receiving a total of 441 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Economics and Econometrics, 11 papers in Statistics and Probability and 10 papers in Environmental Engineering. Recurrent topics in Nan‐Jung Hsu's work include Soil Geostatistics and Mapping (10 papers), Spatial and Panel Data Analysis (8 papers) and Financial Risk and Volatility Modeling (6 papers). Nan‐Jung Hsu is often cited by papers focused on Soil Geostatistics and Mapping (10 papers), Spatial and Panel Data Analysis (8 papers) and Financial Risk and Volatility Modeling (6 papers). Nan‐Jung Hsu collaborates with scholars based in Taiwan, United States and Italy. Nan‐Jung Hsu's co-authors include Ya‐Mei Chang, Hsin‐Cheng Huang, F. Jay Breidt, Noel Cressie, Mark S. Kaiser, Soumendra N. Lahiri, Sheng‐Tsaing Tseng, David M. Theobald, Hans‐Uwe Dahms and Randall C. Cutlip and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Marine Biology.

In The Last Decade

Nan‐Jung Hsu

25 papers receiving 421 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nan‐Jung Hsu Taiwan	12	189	128	119	82	63	28	441
Paolo Vidoni Italy	9	114 0.6×	222 1.7×	55 0.5×	97 1.2×	93 1.5×	36	467
Marco Reale New Zealand	13	121 0.6×	66 0.5×	25 0.2×	121 1.5×	112 1.8×	50	584
Ya‐Mei Chang Taiwan	9	86 0.5×	65 0.5×	31 0.3×	29 0.4×	30 0.5×	19	336
Yeliz Mert Kantar Türkiye	13	80 0.4×	127 1.0×	265 2.2×	51 0.6×	75 1.2×	46	702
Thomas Nagler Germany	10	58 0.3×	76 0.6×	37 0.3×	100 1.2×	75 1.2×	23	377
Rajarshi Guhaniyogi United States	9	124 0.7×	109 0.9×	215 1.8×	13 0.2×	144 2.3×	22	470
Andy Pole United Kingdom	7	53 0.3×	84 0.7×	35 0.3×	32 0.4×	88 1.4×	9	424
Soutir Bandyopadhyay United States	8	108 0.6×	78 0.6×	144 1.2×	14 0.2×	86 1.4×	29	378
Paul L. Anderson United States	16	67 0.4×	55 0.4×	33 0.3×	129 1.6×	47 0.7×	23	548
Athanasios C. Micheas United States	9	41 0.2×	157 1.2×	50 0.4×	28 0.3×	90 1.4×	32	398

Countries citing papers authored by Nan‐Jung Hsu

Since Specialization

Citations

This map shows the geographic impact of Nan‐Jung Hsu'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 Nan‐Jung Hsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nan‐Jung Hsu more than expected).

Fields of papers citing papers by Nan‐Jung Hsu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nan‐Jung Hsu. 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 Nan‐Jung Hsu. The network helps show where Nan‐Jung Hsu may publish in the future.

Co-authorship network of co-authors of Nan‐Jung Hsu

This figure shows the co-authorship network connecting the top 25 collaborators of Nan‐Jung Hsu. A scholar is included among the top collaborators of Nan‐Jung Hsu 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 Nan‐Jung Hsu. Nan‐Jung Hsu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Hsu, Nan‐Jung, et al.. (2024). Covariate-dependent spatio-temporal covariance models. Spatial Statistics. 63. 100853–100853.

2.

Сфарра, Стефано, et al.. (2023). Spatial Structure Analysis for Subsurface Defect Detection in Materials Using Active Infrared Thermography and Adaptive Fixed-Rank Kriging. SHILAP Revista de lepidopterología. 43–43.

3.

Hsu, Nan‐Jung, et al.. (2023). Testing for symmetric correlation matrices with applications to factor models. Journal of Time Series Analysis. 44(5-6). 622–643.

4.

Hsu, Nan‐Jung, et al.. (2021). An item response tree model with not‐all‐distinct end nodes for non‐response modelling. British Journal of Mathematical and Statistical Psychology. 74(3). 487–512. 3 indexed citations

5.

Tseng, Sheng‐Tsaing, et al.. (2021). A generalized pH acceleration model of nano-sol products and the effects of model misspecification on shelf-life prediction. IISE Transactions. 1–58. 1 indexed citations

6.

Tseng, Sheng‐Tsaing, et al.. (2016). Joint modeling of laboratory and field data with application to warranty prediction for highly reliable products. IIE Transactions. 48(8). 710–719. 19 indexed citations

7.

Hsu, Nan‐Jung, Sheng‐Tsaing Tseng, & Mingwei Chen. (2015). Adaptive Warranty Prediction for Highly Reliable Products. IEEE Transactions on Reliability. 64(3). 1057–1067. 6 indexed citations

8.

Chang, Ching‐Hao, et al.. (2015). Power-law ansatz in complex systems: Excessive loss of information. Physical Review E. 92(6). 62925–62925. 6 indexed citations

9.

Chen, Yen-Hung & Nan‐Jung Hsu. (2014). A frequency domain test for detecting nonstationary time series. Computational Statistics & Data Analysis. 75. 179–189. 4 indexed citations

10.

Hsu, Nan‐Jung, Ya‐Mei Chang, & Hsin‐Cheng Huang. (2011). A group lasso approach for non‐stationary spatial–temporal covariance estimation. Environmetrics. 23(1). 12–23. 11 indexed citations

11.

Tseng, Li‐Chun, Hans‐Uwe Dahms, Nan‐Jung Hsu, & Jiang‐Shiou Hwang. (2011). Effects of sedimentation on the gorgonian Subergorgia suberosa (Pallas, 1766). Marine Biology. 158(6). 1301–1310. 13 indexed citations

12.

Chang, Ya‐Mei, Nan‐Jung Hsu, & Hsin‐Cheng Huang. (2010). Semiparametric Estimation of Nonstationary Spatial Covariance Function. Journal of Computational and Graphical Statistics. 117–139. 1 indexed citations

13.

Chang, Ya‐Mei, Nan‐Jung Hsu, & Hsin‐Cheng Huang. (2010). Semiparametric Estimation and Selection for Nonstationary Spatial Covariance Functions. Journal of Computational and Graphical Statistics. 19(1). 117–139. 11 indexed citations

14.

Hsu, Nan‐Jung, et al.. (2008). Semiparametric estimation for seasonal long-memory time series using generalized exponential models. Journal of Statistical Planning and Inference. 139(6). 1992–2009. 15 indexed citations

15.

Breidt, F. Jay, et al.. (2007). A diagnostic test for autocorrelation in increment-averaged data with application to soil sampling. Environmental and Ecological Statistics. 15(1). 15–25. 1 indexed citations

16.

Hsu, Nan‐Jung. (2006). LONG-MEMORY WAVELET MODELS. 9 indexed citations

17.

Huang, Hsin‐Cheng & Nan‐Jung Hsu. (2004). Modeling transport effects on ground‐level ozone using a non‐stationary space–time model. Environmetrics. 15(3). 251–268. 29 indexed citations

18.

Lahiri, Soumendra N., Mark S. Kaiser, Noel Cressie, & Nan‐Jung Hsu. (1999). Prediction of Spatial Cumulative Distribution Functions Using Subsampling. Journal of the American Statistical Association. 94(445). 86–97. 69 indexed citations

19.

Lahiri, Soumendra N., Mark S. Kaiser, Noel Cressie, & Nan‐Jung Hsu. (1999). Prediction of Spatial Cumulative Distribution Functions Using Subsampling. Journal of the American Statistical Association. 94(445). 86–86. 19 indexed citations

20.

Kaiser, Mark S., Nan‐Jung Hsu, Noel Cressie, & Soumendra N. Lahiri. (1997). Inference for Spatial Processes Using Subsampling: a Simulation Study. Environmetrics. 8(5). 485–502. 9 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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