R. J. A. Little

438 total citations
10 papers, 283 citations indexed

About

R. J. A. Little is a scholar working on Statistics and Probability, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, R. J. A. Little has authored 10 papers receiving a total of 283 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Statistics and Probability, 3 papers in Artificial Intelligence and 2 papers in Economics and Econometrics. Recurrent topics in R. J. A. Little's work include Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (4 papers) and Bayesian Methods and Mixture Models (2 papers). R. J. A. Little is often cited by papers focused on Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (4 papers) and Bayesian Methods and Mixture Models (2 papers). R. J. A. Little collaborates with scholars based in United States. R. J. A. Little's co-authors include Hui Zheng, Michael R. Elliott, Steven G. Heeringa, James M. Lepkowski, Ronald C. Kessler, Steve Lewitzky and Fang Liu and has published in prestigious journals such as Journal of the American Statistical Association, American Journal of Epidemiology and Population Studies.

In The Last Decade

R. J. A. Little

10 papers receiving 254 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
R. J. A. Little United States	8	162	77	51	43	27	10	283
Yves G. Berger United Kingdom	13	312 1.9×	107 1.4×	75 1.5×	89 2.1×	20 0.7×	51	483
Elizabeth A. Stasny United States	12	309 1.9×	73 0.9×	32 0.6×	45 1.0×	18 0.7×	34	499
Guillaume Chauvet France	10	142 0.9×	40 0.5×	38 0.7×	40 0.9×	19 0.7×	38	277
Howard Hogan United States	8	158 1.0×	104 1.4×	36 0.7×	14 0.3×	58 2.1×	13	292
Bart Bakker Netherlands	7	83 0.5×	109 1.4×	32 0.6×	21 0.5×	64 2.4×	18	268
Angelo Mazza Italy	10	91 0.6×	83 1.1×	52 1.0×	92 2.1×	7 0.3×	26	281
Harold Nisselson United States	6	122 0.8×	77 1.0×	56 1.1×	34 0.8×	35 1.3×	8	311
Catherine A Fitch United States	9	32 0.2×	140 1.8×	43 0.8×	38 0.9×	72 2.7×	25	344
Cristina Sotto Belgium	7	225 1.4×	24 0.3×	29 0.6×	63 1.5×	6 0.2×	11	312
E. C. Marshall United Kingdom	6	105 0.6×	12 0.2×	105 2.1×	55 1.3×	61 2.3×	9	373

Countries citing papers authored by R. J. A. Little

Since Specialization

Citations

This map shows the geographic impact of R. J. A. Little'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 R. J. A. Little with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. J. A. Little more than expected).

Fields of papers citing papers by R. J. A. Little

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by R. J. A. Little. 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 R. J. A. Little. The network helps show where R. J. A. Little may publish in the future.

Co-authorship network of co-authors of R. J. A. Little

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

All Works

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10 of 10 papers shown

Zheng, Hui & R. J. A. Little. (2003). Penalized Spline Model-Based Estimation of the Finite Populations Total from Probability-Proportional-to-Size Samples. Journal of Official Statistics. 19(2). 99–9. 37 indexed citations

Zheng, Hui & R. J. A. Little. (2003). Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model. Journal of Official Statistics. 21(1). 1. 32 indexed citations

Zheng, Hui & R. J. A. Little. (2003). Penalized Spline Nonparametric Mixed Models for Inference About a Finite Population Mean from Two-Stage Samples. Collection of Biostatistics Research Archive. 32 indexed citations

Little, R. J. A. & Fang Liu. (2003). Selective Multiple Imputation of Keys for Statistical Disclosure Control in Microdata. Collection of Biostatistics Research Archive. 19 indexed citations

Little, R. J. A., et al.. (2003). WEIGHTING ADJUSTMENTS FOR UNIT NONRESPONSE WITH MULTIPLE OUTCOME VARIABLES. Collection of Biostatistics Research Archive. 1 indexed citations

Little, R. J. A., et al.. (2003). On the Formation of Weighting Adjustment Cells for Unit Nonresponse. Collection of Biostatistics Research Archive. 14 indexed citations

Elliott, Michael R. & R. J. A. Little. (2000). Model-Based Alternatives to Trimming Survey Weights. Journal of Official Statistics. 16(3). 191–210. 49 indexed citations

Little, R. J. A., Steve Lewitzky, Steven G. Heeringa, James M. Lepkowski, & Ronald C. Kessler. (1997). Assessment of Weighting Methodology for the National Comorbidity Survey. American Journal of Epidemiology. 146(5). 439–449. 65 indexed citations

Little, R. J. A.. (1993). Post-Stratification: A Modeler's Perspective. Journal of the American Statistical Association. 88(423). 1001–1001. 27 indexed citations

10.

Little, R. J. A., et al.. (1984). Fertility Exposure Analysis: A New Method for Assessing the Contribution of Proximate Determinants to Fertility Differentials. Population Studies. 38(1). 21–21. 7 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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