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A review of recent advances in empirical likelihood. (English) Zbl 07910641


MSC:

62-08 Computational methods for problems pertaining to statistics

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vxdbel
Full Text: DOI

References:

[1] Adimari, G., & Chiogna, M. (2012). Jackknife empirical likelihood based confidence intervals for partial areas under ROC curves. Statistica Sinica, 22, 1457-1477. · Zbl 1534.62059
[2] Alemdjrodo, K., & Zhao, Y. (2019). Reduce the computation in jackknife empirical likelihood for comparing two correlated Gini indices. Journal of Nonparametric Statistics, 31, 849-866. · Zbl 1432.62066
[3] Alemdjrodo, K., & Zhao, Y. (2020). New empirical likelihood inference for the mean residual life with length‐biased and right‐censored data. Journal of Nonparametric Statistics, 32, 1029-1046. · Zbl 1466.62273
[4] Alemdjrodo, K., & Zhao, Y. (2022). Novel empirical likelihood inference for the mean difference with right‐censored data. Statistical Methods in Medical Research, 31, 87-104.
[5] An, Y., & Zhao, Y. (2018). Jackknife empirical likelihood for the difference of two volumes under ROC surfaces. Annals of the Institute of Statistical Mathematics, 70, 789-806. · Zbl 1406.62132
[6] Anatolyev, S. (2005). GMM, GEL, serial correlation, and asymptotic bias. Econometrica, 73, 983-1002. · Zbl 1152.62360
[7] Bartolucci, F. (2007). A penalized version of the empirical likelihood ratio for the population mean. Statistics & Probability Letters, 77, 104-110. · Zbl 1106.62050
[8] Bedoui, A., & Lazar, N. A. (2020). Bayesian empirical likelihood for ridge and lasso regressions. Computational Statistics and Data Analysis, 145, 106917. · Zbl 1510.62307
[9] Berger, Y. G. (2015). An R library to construct empirical likelihood confidence intervals for complex estimators. In New techniques and technologies for statistics. Statistical Sciences Research Institute.
[10] Berger, Y. G., & Torres, O. D. L. R. (2016). Empirical likelihood confidence intervals for complex sampling designs. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 78, 319-341. · Zbl 1414.62046
[11] Bouadoumou, M., Zhao, Y., & Lu, Y. (2015). Jackknife empirical likelihood for the accelerated failure time model with censored data. Communications in Statistics—Simulation and Computation, 44, 1818-1832. · Zbl 1327.62484
[12] Bravo, F., Escanciano, J. C., & Keilegom, I. V. (2020). Two‐step semiparametric empirical likelihood inference. The Annals of Statistics, 48, 1-26. · Zbl 1439.62188
[13] Bühlmann, P., & Geer, S. v. d. (2011). Statistics for high‐dimensional data: Methods, theory and applications. Springer. · Zbl 1273.62015
[14] Cappé, O., Garivier, A., Maillard, O.‐A., Munos, R., & Stoltz, G. (2013). Kullback‐leibler upper confidence bounds for optimal sequential allocation. The Annals of Statistics, 41, 1516-1541. · Zbl 1293.62161
[15] Chang, J., Chen, S. X., & Chen, X. (2015). High dimensional generalized empirical likelihood for moment restrictions with dependent data. Journal of Econometrics, 185, 283-304. · Zbl 1331.62188
[16] Chang, J., Chen, S. X., Tang, C. Y., & Wu, T. T. (2021). High‐dimensional empirical likelihood inference. Biometrika, 108, 127-147. · Zbl 1462.62760
[17] Chang, J., Tang, C. Y., & Wu, T. T. (2018). A new scope of penalized empirical likelihood with high‐dimensional estimating equations. The Annals of Statistics, 46, 3185-3216. · Zbl 1408.62053
[18] Chaudhuri, S., & Ghosh, M. (2011). Empirical likelihood for small area estimation. Biometrika, 98, 473-480. · Zbl 1215.62031
[19] Chaudhuri, S., Mondal, D., & Yin, T. (2017). Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation. Journal of the Royal Statistical Society: Series B, 79, 293-320. · Zbl 1414.62333
[20] Chaussé, P. (2010). Computing generalized method of moments and generalized empirical likelihood with R. Journal of Statistical Software, 34, 1-35.
[21] Chen, B., Pan, G., Yang, Q., & Zhou, W. (2015). Large dimensional empirical likelihood. Statistica Sinica, 25, 1659-1677. · Zbl 1377.62127
[22] Chen, J., & Huang, Y. (2013). Finite‐sample properties of the adjusted empirical likelihood. Journal of Nonparametric Statistics, 25, 147-159. · Zbl 1297.62105
[23] Chen, J., & Liu, Y. (2012). Adjusted empirical likelihood with high‐order one‐sided coverage precision. Statistics and its Interface, 5, 281-292. · Zbl 1383.62141
[24] Chen, J., Variyath, A. M., & Abraham, B. (2008). Adjusted empirical likelihood and its properties. Journal of Computational and Graphical Statistics, 17, 426-443.
[25] Chen, S. (1993). On the accuracy of empirical likelihood confidence regions for linear regression model. Annals of the Institute of Statistical Mathematics, 45, 621-637. · Zbl 0799.62070
[26] Chen, S., & Cui, H. (2006). On Bartlett correction of empirical likelihood in the presence of nuisance parameters. Biometrika, 93, 215-220. · Zbl 1152.62325
[27] Chen, S., & Cui, H. (2007). On the second‐order properties of empirical likelihood with moment restrictions. Journal of Econometrics, 141, 492-516. · Zbl 1407.62157
[28] Chen, S., & Hall, P. (1993). Smoothed empirical likelihood confidence intervals for quantiles. The Annals of Statistics, 21, 1166-1181. · Zbl 0786.62053
[29] Chen, S., & Haziza, D. (2018). Jackknife empirical likelihood method for multiply robust estimation with missing data. Computational Statistics and Data Analysis, 127, 258-268. · Zbl 1469.62042
[30] Chen, S., Peng, L., & Qin, Y.‐L. (2009). Effects of data dimension on empirical likelihood. Biometrika, 96, 711-722. · Zbl 1170.62023
[31] Chen, S., Zhao, Y., & Wang, Y. (2021). Sample empirical likelihood approach under complex survey design with scrambled responses. Survey Methodology, 47, 59-74.
[32] Chen, S. X., & Keilegom, I. V. (2009). A review on empirical likelihood methods for regression. TEST, 18, 415-447. · Zbl 1203.62035
[33] Chen, Y.‐J., Ning, W., & Gupta, A. K. (2015). Jackknife empirical likelihood method for testing the equality of two variances. Journal of Applied Statistics, 42, 144-160. · Zbl 1514.62488
[34] Cheng, C., Liu, Y., Liu, Z., & Zhou, W. (2018). Balanced augmented jackknife empirical likelihood for two sample U‐statistics. Science China Mathematics, 61, 1129-1138. · Zbl 1394.62051
[35] Cheng, Y., & Zhao, Y. (2019). Bayesian jackknife empirical likelihood. Biometrika, 106, 981-988. · Zbl 1435.62378
[36] Ciuperca, G., & Salloum, Z. (2016). Empirical likelihood test for high‐dimensional two‐sample model. Journal of Statistical Planning and Inference, 178, 37-60. · Zbl 1346.62026
[37] Claeskens, G., Jing, B.‐Y., Peng, L., & Zhou, W. (2003). Empirical likelihood confidence regions for comparison distributions and ROC curves. The Canadian Journal of Statistics, 31, 173-190. · Zbl 1039.62038
[38] Cui, X., Li, R., Yang, G., & Zhou, W. (2020). Empirical likelihood test for a large‐dimensional mean vector. Biometrika, 107, 591-607. · Zbl 1451.62062
[39] Dai, B., Nachum, O., Chow, Y., Li, L., Szepesvari, C., & Schuurmans, D. (2020). Coindice: Off‐policy confidence interval estimation. In Advances in neural information processing systems (Vol. 33, pp. 9398-9411). Curran Associates, Inc.
[40] Defor, E., & Zhao, Y. (2022). Empirical likelihood inference for the mean past lifetime function. Statistics, 56, 329-346. · Zbl 1493.62234
[41] DiCiccio, T., Hall, P., & Romano, J. (1991). Empirical likelihood is Bartlett‐correctable. The Annals of Statistics, 19, 1053-1061. · Zbl 0725.62042
[42] Ding, L., Liu, Z., Li, Y., Liao, S., Liu, Y., Yang, P., Yu, G., Shao, L., & Gao, X. (2019). Linear kernel tests via empirical likelihood for high‐dimensional data. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 3454-3461.
[43] Emerson, S. C., & Owen, A. B. (2009). Calibration of the empirical likelihood method for a vector mean. Electronic Journal of Statistics, 3, 1161-1192. · Zbl 1326.62099
[44] Fan, G.‐L., Liang, H.‐Y., & Yu, S. (2016). Penalized empirical likelihood for high‐dimensional partially linear varying coefficient model with measurement errors. Journal of Multivariate Analysis, 147, 183-201. · Zbl 1334.62059
[45] Fan, J., & Lv, J. (2010). A selective overview of variable selection in high dimensional feature space. Statistica Sinica, 20, 101-148. · Zbl 1180.62080
[46] Feng, H., & Peng, L. (2012a). Jackknife empirical likelihood tests for distribution functions. Journal of Statistical Planning and Inference, 142, 1571-1585. · Zbl 1242.62032
[47] Feng, H., & Peng, L. (2012b). Jackknife empirical likelihood tests for error distributions in regression models. Journal of Multivariate Analysis, 112, 63-75. · Zbl 1273.62039
[48] Gamage, R. D. P., & Ning, W. (2020). Inference for long‐memory time series models based on modified empirical likelihood. Austrian Journal of Statistics, 49, 68-79.
[49] Gong, Y., Peng, L., & Qi, Y. (2010). Smoothed jackknife empirical likelihood method for ROC curve. Journal of Multivariate Analysis, 101, 1520-1531. · Zbl 1186.62053
[50] Guo, H., Zou, C., Wang, Z., & Chen, B. (2014). Empirical likelihood for high‐dimensional linear regression models. Metrika, 77, 921-945. · Zbl 1305.62143
[51] Hall, P., & Scala, B. L. (1990). Methodology and algorithms of empirical likelihood. International Statistical Review, 58, 109-127. · Zbl 0716.62003
[52] He, S., Liang, W., Shen, J., & Yang, G. (2016). Empirical likelihood for right censored lifetime data. Journal of the American Statistical Association, 111, 646-655.
[53] Hjort, N. L., McKeague, I. W., & Keilegom, I. V. (2009). Extending the scope of empirical likelihood. The Annals of Statistics, 37, 1079-1111. · Zbl 1160.62029
[54] Huang, H., & Zhao, Y. (2018). Empirical likelihood for the bivariate survival function under univariate censoring. Journal of Statistical Planning and Inference, 194, 32-46. · Zbl 1392.62295
[55] Jiang, H., & Zhao, Y. (2022a). Bayesian jackknife empirical likelihood for the error variance in linear regression models. Journal of Statistical Computation and Simulation, in press. · Zbl 07632293
[56] Jiang, H., & Zhao, Y. (2022b). Transformed jackknife empirical likelihood for probability weighted moments. Journal of Statistical Computation and Simulation, 92, 1618-1639. · Zbl 07546448
[57] Jiang, Y., Wang, S., Ge, W., & Wang, X. (2011). Depth‐based weighted empirical likelihood and general estimating equations. Journal of Nonparametric Statistics, 23, 1051-1062. · Zbl 1228.62038
[58] Jing, B.‐Y., Tsao, M., & Zhou, W. (2017). Transforming the empirical likelihood towards better accuracy. The Canadian Journal of Statistics, 45, 340-352. · Zbl 1474.62155
[59] Jing, B.‐Y., Yuan, J., & Zhou, W. (2009). Jackknife empirical likelihood. Journal of the American Statistical Association, 104, 1224-1232. · Zbl 1388.62136
[60] Kallus, N., & Uehara, M. (2019). Intrinsically efficient, stable, and bounded off‐policy evaluation for reinforcement learning. In Advances in neural information processing systems (Vol. 32). Curran Associates, Inc.
[61] Karampatziakis, N., Langford, J., & Mineiro, P. (2020). Empirical likelihood for contextual bandits. In 34th conference on neural information processing systems (Vol. 33, pp. 9597-9607). Curran Associates, Inc.
[62] Kitamura, Y. (2007). Empirical likelihood methods in econometrics: Theory and practice. In R.Blundell (ed.), W. K.Newey (ed.), & T.Persson (ed.) (Eds.), Advances in economics and econometrics: Ninth world congress of the econometric society (pp. 174-237). Cambridge University Press. · Zbl 1131.62106
[63] Lahiri, S. N., & Mukhopadhyay, S. (2012). A penalized empirical likelihood method in high dimensions. The Annals of Statistics, 40, 2511-2540. · Zbl 1373.62132
[64] Lancaster, T., & Jun, S. J. (2010). Bayesian quantile regression methods. Journal of Applied Econometrics, 25, 287-307.
[65] Lazar, N. A. (2003). Bayesian empirical likelihood. Biometrika, 90, 319-326. · Zbl 1034.62020
[66] Lazar, N. A. (2021). A review of empirical likelihood. Annual Review of Statistics and its Application, 8, 329-344.
[67] Lazar, N. A., & Mykland, P. A. (1999). Empirical likelihood in the presence of nuisance parameters. Biometrika, 86, 203-211. · Zbl 0917.62029
[68] Leng, C., & Tang, C. Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika, 99, 703-716. · Zbl 1437.62522
[69] Li, G., Lin, L., & Zhu, L. (2012). Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters. Journal of Multivariate Analysis, 105, 85-111. · Zbl 1236.62020
[70] Li, M., Peng, L., & Qi, Y. (2011). Reduce computation in profile empirical likelihood method. The Canadian Journal of Statistics, 39, 370-384. · Zbl 1271.62072
[71] Liang, W., & Dai, H. (2021). Empirical likelihood based on synthetic right censored data. Statistics and Probability Letters, 169, 108962. · Zbl 1456.62232
[72] Liang, W., Dai, H., & He, S. (2019). Mean empirical likelihood. Computational Statistics and Data Analysis, 138, 155-169. · Zbl 1507.62103
[73] Liang, W., & He, S. (2018). Mean empirical likelihood. Advances in Mathematics (China), 47, 287-295. · Zbl 1413.62058
[74] Lin, H.‐L., Li, Z., Wang, D., & Zhao, Y. (2017). Jackknife empirical likelihood for the error variance in linear models. Journal of Nonparametric Statistics, 29, 151-166. · Zbl 1369.62060
[75] Liu, A., & Liang, H. (2017). Jackknife empirical likelihood of error variance in partially linear varying‐coefficient errors‐in‐variables models. Statistical Papers, 58, 95-122. · Zbl 1357.62255
[76] Liu, X., & Zhao, Y. (2012). Semi‐empirical likelihood inference for the ROC curve with missing data. Journal of Statistical Planning and Inference, 142, 3123-3133. · Zbl 1348.62255
[77] Liu, Y., & Chen, J. (2010). Adjusted empirical likelihood with high‐order precision. The Annals of Statistics, 38, 1341-1362. · Zbl 1189.62054
[78] Liu, Y., & Yu, C. W. (2010). Bartlett correctable two‐sample adjusted empirical likelihood. Journal of Multivariate Analysis, 101, 1701-1711. · Zbl 1189.62084
[79] Liu, Y., Zou, C., & Wang, Z. (2013). Calibration of the empirical likelihood for high‐dimensional data. Annals of the Institute of Statistical Mathematics, 65, 529-550. · Zbl 1396.62105
[80] Lopez, E. M. M., Keilegom, I. V., & Veraverbeke, N. (2009). Empirical likelihood for non‐smooth criterion functions. Scandinavian Journal of Statistics, 36, 413-432. · Zbl 1194.62069
[81] Lv, X., Zhang, G., Xu, X., & Li, Q. (2017). Bootstrap‐calibrated empirical likelihood confidence intervals for the difference between two Gini indexes. The Journal of Economic Inequality, 15, 195-216.
[82] Matsushita, Y., & Otsu, T. (2021). Jackknife empirical likelihood: Small bandwidth, sparse network and high‐dimensional asymptotics. Biometrika, 108, 661-674. · Zbl 07459721
[83] Newey, W. K., & Smith, R. J. (2004). Higher order properties of gmm and generalized empirical likelihood estimators. Econometrica, 72, 219-255. · Zbl 1151.62313
[84] Nordman, D. J., & Lahiri, S. N. (2014). A review of empirical likelihood methods for time series. Journal of Statistical Planning and Inference, 155, 1-18. · Zbl 1307.62120
[85] Otsu, T. (2007). Penalized empirical likelihood estimation of semiparametric models. Journal of Multivariate Analysis, 98, 1923-1954. · Zbl 1138.62015
[86] Owen, A. B. (1988). Empirical likelihood ratio confidence intervals for a single functional. Biometrika, 75, 237-249. · Zbl 0641.62032
[87] Owen, A. B. (1990). Empirical likelihood ratio confidence regions. The Annals of Statistics, 18, 90-120. · Zbl 0712.62040
[88] Owen, A. B. (1991). Empirical likelihood for linear models. The Annals of Statistics, 19, 1725-1747. · Zbl 0799.62048
[89] Owen, A. B. (2001). Empirical likelihood. Chapman and Hall/CRC. · Zbl 0989.62019
[90] Owen, A. B. (2013). Self‐concordance for empirical likelihood. The Canadian Journal of Statistics, 41, 387-397. · Zbl 1273.62072
[91] Parente, P. M., & Smith, R. J. (2014). Recent developments in empirical likelihood and related methods. Annual Review of Economics, 6, 77-102.
[92] Peng, L. (2011). Empirical likelihood methods for the Gini index. Australian & New Zealand Journal of Statistics, 53, 131-139. · Zbl 1241.91101
[93] Peng, L. (2012). Approximate jackknife empirical likelihood method for estimating equations. The Canadian Journal of Statistics, 40, 100-123. · Zbl 1274.62225
[94] Peng, L., & Qi, Y. (2010). Smoothed jackknife empirical likelihood method for tail copulas. TEST, 19, 514-536. · Zbl 1203.62091
[95] Peng, L., Qi, Y., & Keilegom, I. V. (2012). Jackknife empirical likelihood method for copulas. TEST, 21, 74-92. · Zbl 1259.62026
[96] Peng, L., Qi, Y., & Wang, R. (2014). Empirical likelihood test for high dimensional linear models. Statistics and Probability Letters, 86, 85-90. · Zbl 1288.62071
[97] Priam, R.. (2021). A brief survey of numerical procedures for empirical likelihood. Hal‐03095014.
[98] Qin, G., & Zhao, Y. (2007). Empirical likelihood inference for the mean residual life under random censorship. Statistics and Probability Letters, 77, 549-557. · Zbl 1113.62122
[99] Qin, G., & Zhou, X.‐H. (2006). Empirical likelihood inference for the area under the ROC curve. Biometrics, 62, 613-622. · Zbl 1097.62099
[100] Qin, J., & Lawless, J. (1994). Empirical likelihood and general estimating equations. The Annals of Statistics, 22, 300-325. · Zbl 0799.62049
[101] Qin, Y., Rao, J., & Wu, C. (2010). Empirical likelihood confidence intervals for the Gini measure of income inequality. Economic Modelling, 27, 1429-1435.
[102] Rahman, H., & Zhao, Y. (2022). Empirical likelihood confidence interval for sensitivity to the early disease stage. Pharmaceutical Statistics, 21, 566-583.
[103] Rao, J. N. K., & Wu, C. (2010). Bayesian pseudo‐empirical‐likelihood intervals for complex surveys. Journal of the Royal Statistical Society: Series B, 72, 533-544. · Zbl 1411.62034
[104] Sang, Y. (2021). A jackknife empirical likelihood approach for testing the homogeneity of K variances. Metrika, 84, 1025-1048. · Zbl 1475.62155
[105] Sang, Y., Dang, X., & Zhao, Y. (2019). Jackknife empirical likelihood methods for Gini correlations and their equality testing. Journal of Statistical Planning and Inference, 199, 45-59. · Zbl 1418.62221
[106] Sang, Y., Dang, X., & Zhao, Y. (2020). Depth‐based weighted jackknife empirical likelihood for non‐smooth U‐structure equations. TEST, 29, 573-598. · Zbl 1447.62049
[107] Sang, Y., Dang, X., & Zhao, Y. (2021). A jackknife empirical likelihood approach for K‐sample tests. The Canadian Journal of Statistics, 49, 1115-1135. · Zbl 1492.62087
[108] Satter, F., & Zhao, Y. (2020). Nonparametric interval estimation for the mean of a zero‐inflated population. Communications in Statistics—Simulation and Computation, 49, 2059-2067. · Zbl 07552784
[109] Satter, F., & Zhao, Y. (2021). Jackknife empirical likelihood for the mean difference of two zero‐inflated skewed populations. Journal of Statistical Planning and Inference, 221, 414-422. · Zbl 1455.62096
[110] Schennach, S. M. (2005). Bayesian exponentially tilted empirical likelihood. Biometrika, 92, 31-46. · Zbl 1068.62035
[111] Schennach, S. M. (2007). Point estimation with exponentially tilted empirical likelihood. The Annals of Statistics, 35, 634-672. · Zbl 1117.62024
[112] Sheng, Y., Sun, Y., Huang, C.‐Y., & Kim, M.‐O. (2021). Synthesizing external aggregated information in the penalized Cox regression under population heterogeneity. Statistics in Medicine, 40, 4915-4930.
[113] Sheng, Y., Sun, Y., Huang, C.‐Y., & Kim, M.‐O. (2022). Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach. Biometrics, 78(2), 679-690. · Zbl 1520.62323
[114] Shi, Z. (2016). Econometric estimation with high‐dimensional moment equalities. Journal of Econometrics, 195, 104-119. · Zbl 1443.62506
[115] Smith, R. J. (1997). Alternative semi‐parametric likelihood approaches to generalised method of moments estimation. The Economic Journal, 107, 503-519.
[116] Sreelakshmi, N., Kattumannil, S. K., & Sen, R. (2021). Jackknife empirical likelihood‐based inference for S‐Gini indices. Communications in Statistics—Simulation and Computation, 50, 1645-1661. · Zbl 1497.62341
[117] Stewart, P., & Ning, W. (2020a). Confidence intervals for data containing many zeros and ones based on empirical likelihood‐type methods. Journal of Statistical Computation and Simulation, 90, 3376-3399. · Zbl 07481517
[118] Stewart, P., & Ning, W. (2020b). Modified empirical likelihood‐based confidence intervals for data containing many zero observations. Computational Statistics, 35, 2019-2042. · Zbl 1505.62386
[119] Sun, Y., Sundaram, R., & Zhao, Y. (2009). Empirical likelihood inference for the Cox model with time‐dependent coefficients via local partial likelihood. Scandinavian Journal of Statistics, 36, 444-462. · Zbl 1198.62138
[120] Tang, C. Y., & Leng, C. (2010). Penalized high‐dimensional empirical likelihood. Biometrika, 97, 905-919. · Zbl 1204.62050
[121] Tang, C. Y., & Qin, Y. (2012). An efficient empirical likelihood approach for estimating equations with missing data. Biometrika, 99, 1001-1007. · Zbl 1452.62175
[122] Tang, C. Y., & Wu, T. T. (2014). Nested coordinate descent algorithms for empirical likelihood. Journal of Statistical Computation and Simulation, 84, 1917-1930. · Zbl 1453.62436
[123] Tang, X., Li, J., & Lian, H. (2013). Empirical likelihood for partially linear proportional hazards models with growing dimensions. Journal of Multivariate Analysis, 121, 22-32. · Zbl 1328.62569
[124] Thorne, T. (2015). Empirical likelihood tests for nonparametric detection of differential expression from rna‐seq data. Statistical Applications in Genetics and Molecular Biology, 14, 575-583. · Zbl 1330.92013
[125] Tsao, M. (2004). Bounds on coverage probabilities of the empirical likelihood ratio confidence regions. The Annals of Statistics, 32, 1215-1221. · Zbl 1091.62040
[126] Tsao, M. (2013). Extending the empirical likelihood by domain expansion. The Canadian Journal of Statistics, 41, 257-274. · Zbl 1273.62076
[127] Tsao, M., & Wu, F. (2013). Empirical likelihood on the full parameter space. The Annals of Statistics, 41, 2176-2196. · Zbl 1360.62140
[128] Tsao, M., & Wu, F. (2014). Extended empirical likelihood for estimating equations. Biometrika, 101, 703-710. · Zbl 1334.62044
[129] Tsao, M., & Wu, F. (2015). Two‐sample extended empirical likelihood for estimating equations. Journal of Multivariate Analysis, 142, 1-15. · Zbl 1327.62312
[130] Vexler, A., & Tanajian, H. (2014). Density‐based empirical likelihood procedures for testing symmetry of data distributions and K‐sample comparisons. The Stata Journal, 14, 304-328.
[131] Vexler, A., & Yu, J. (2018). Empirical likelihood methods in biomedicine and health. Chapman and Hall/CRC.
[132] Vexler, A., Yu, J., & Lazar, N. (2017). Bayesian empirical likelihood methods for quantile comparisons. Journal of the Korean Statistical Society, 46, 518-538. · Zbl 1377.62130
[133] Wang, D., Tian, L., & Zhao, Y. (2017). Smoothed empirical likelihood for the Youden index. Computational Statistics and Data Analysis, 115, 1-10. · Zbl 1466.62209
[134] Wang, D., Wu, T. T., & Zhao, Y. (2019). Penalized empirical likelihood for the sparse Cox regression model. Journal of Statistical Planning and Inference, 201, 71-85. · Zbl 1421.62083
[135] Wang, D., & Zhao, Y. (2016). Jackknife empirical likelihood for comparing two Gini indices. The Canadian Journal of Statistics, 44, 102-119. · Zbl 1357.62082
[136] Wang, L. (2017). Bartlett‐corrected two‐sample adjusted empirical likelihood via resampling. Communications in Statistics—Theory and Methods, 46, 10941-10952. · Zbl 1462.62219
[137] Wang, L., Chen, J., & Pu, X. (2015). Resampling calibrated adjusted empirical likelihood. The Canadian Journal of Statistics, 43, 42-59. · Zbl 1310.62060
[138] Wang, L., Li, W., Liu, G., & Pu, X. (2015). Spatial median depth‐based robust adjusted empirical likelihood. Journal of Nonparametric Statistics, 27, 485-502. · Zbl 1328.62222
[139] Wang, Q.‐H., & Jing, B.‐Y. (2001). Empirical likelihood for a class of functionals of survival distribution with censored data. Annals of the Institute of Statistical Mathematics, 53, 517-527. · Zbl 1009.62092
[140] Wang, R., & Peng, L. (2011). Jackknife empirical likelihood intervals for Spearman’s rho. North American Actuarial Journal, 15, 475-486. · Zbl 1291.62117
[141] Wang, R., Peng, L., & Qi, Y. (2013). Jackknife empirical likelihood test for equality of two high dimensional means. Statistica Sinica, 23, 667-690. · Zbl 1379.62041
[142] Wang, S., Wang, H., Zhao, Y., Cao, G., & Li, Y. (2021). Empirical likelihood ratio tests for varying coefficient geo models. Statistica Sinica, in press.
[143] Wu, C. (2005). Algorithms and R codes for the pseudo empirical likelihood method in survey sampling. Survey Methodology, 31, 239-243.
[144] Wu, F., & Tsao, M. (2014). Two‐sample extended empirical likelihood. Statistics and Probability Letters, 84, 81-87. · Zbl 1407.62159
[145] Xu, M., & Chen, L. (2018). An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA‐seq. Briefings in Bioinformatics, 19, 109-117.
[146] Xue, L. (2009). Empirical likelihood confidence intervals for response mean with data missing at random. Scandinavian Journal of Statistics, 36, 671-685. · Zbl 1223.62055
[147] Xue, L., & Xue, D. (2011). Empirical likelihood for semiparametric regression model with missing response data. Journal of Multivariate Analysis, 102, 723-740. · Zbl 1327.62231
[148] Xue, L., & Zhu, L. (2007). Empirical likelihood semiparametric regression analysis for longitudinal data. Biometrika, 94, 921-937. · Zbl 1156.62324
[149] Xue, L., & Zhu, L. (2012). Empirical likelihood in some nonparametric and semiparametric models. Statistics and Its Interface, 5, 367-378. · Zbl 1383.62093
[150] Yang, D., & Small, D. S. (2013). An R package and a study of methods for computing empirical likelihood. Journal of Statistical Computation and Simulation, 83, 1363-1372. · Zbl 1431.62011
[151] Yang, G., Cui, X., & Hou, S. (2017). Empirical likelihood confidence regions in the single‐index model with growing dimensions. Communications in Statistics—Theory and Methods, 46, 7562-7579. · Zbl 1373.62363
[152] Yang, H., Lu, K., & Zhao, Y. (2017). A nonparametric approach for partial areas under ROC curves and ordinal dominance curves. Statistica Sinica, 27, 357-371. · Zbl 1359.62496
[153] Yang, H., Yau, C., & Zhao, Y. (2014). Smoothed empirical likelihood inference for the difference of two quantiles with right censoring. Journal of Statistical Planning and Inference, 146, 95-101. · Zbl 1408.62091
[154] Yang, H., & Zhao, Y. (2012). Smoothed empirical likelihood for ROC curves with censored data. Journal of Multivariate Analysis, 109, 254-263. · Zbl 1241.62036
[155] Yang, H., & Zhao, Y. (2013). Smoothed jackknife empirical likelihood inference for the difference of ROC curves. Journal of Multivariate Analysis, 115, 270-284. · Zbl 1259.62027
[156] Yang, H., & Zhao, Y. (2015). Smoothed jackknife empirical likelihood inference for ROC curves with missing data. Journal of Multivariate Analysis, 140, 123-138. · Zbl 1327.62313
[157] Yang, H., & Zhao, Y. (2017). Smoothed jackknife empirical likelihood for the difference of two quantiles. Annals of the Institute of Statistical Mathematics, 69, 1059-1073. · Zbl 1387.62062
[158] Yang, H., & Zhao, Y. (2018). Smoothed jackknife empirical likelihood for the one‐sample difference of quantiles. Computational Statistics and Data Analysis, 120, 58-69. · Zbl 1469.62168
[159] Yang, K., Ding, X., & Yuan, X. (2022). Bayesian empirical likelihood inference and order shrinkage for autoregressive models. Statistical Papers, 63, 97-121. · Zbl 07504785
[160] Yang, S., & Prentice, R. (2005). Semiparametric analysis of short‐term and long‐term hazard ratios with two‐sample survival data. Biometrika, 92, 1-17. · Zbl 1068.62102
[161] Yang, Y., & He, X. (2012). Bayesian empirical likelihood for quantile regression. The Annals of Statistics, 40, 1102-1131. · Zbl 1274.62458
[162] Yiu, A., Goudie, R. J. B., & Tom, B. D. M. (2020). Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood. Biometrika, 107, 857-873. · Zbl 1457.62230
[163] Yu, X., & Zhao, Y. (2019a). Empirical likelihood inference for semi‐parametric transformation models with length‐biased sampling. Computational Statistics and Data Analysis, 132, 115-125. · Zbl 1507.62200
[164] Yu, X., & Zhao, Y. (2019b). Jackknife empirical likelihood inference for the accelerated failure time model. TEST, 28, 269-288. · Zbl 1420.62216
[165] Zang, Y., Zhang, S., Li, Q., & Zhang, Q. (2016). Jackknife empirical likelihood test for high‐dimensional regression coefficients. Computational Statistics and Data Analysis, 94, 302-316. · Zbl 1468.62226
[166] Zedlewski, J. (2008). Practical empirical likelihood estimation with matElike [Manuscript]. Harvard University.
[167] Zhang, R., Peng, L., & Qi, Y. (2012). Jackknife‐blockwise empirical likelihood methods under dependence. Journal of Multivariate Analysis, 104, 56-72. · Zbl 1352.62053
[168] Zhang, Y., & Tang, N. (2017). Bayesian empirical likelihood estimation of quantile structural equation models. Journal of Systems Science and Complexity, 30, 122-138. · Zbl 1370.93304
[169] Zhao, P., Ghosh, M., Rao, J. N. K., & Wu, C. (2020). Bayesian empirical likelihood inference with complex survey data. Journal of the Royal Statistical Society: Series B, 82, 155-174. · Zbl 1440.62403
[170] Zhao, Y., & Huang, Y. (2007). Test‐based interval estimation under the accelerated failure time model. Communications in Statistics—Simulation and Computation, 36, 593-605. · Zbl 1121.62085
[171] Zhao, Y., & Jinnah, A. (2012). Inference for Cox’s regression models via adjusted empirical likelihood. Computational Statistics, 27, 1-12. · Zbl 1304.65094
[172] Zhao, Y., Meng, X., & Yang, H. (2015). Jackknife empirical likelihood inference for the mean absolute deviation. Computational Statistics and Data Analysis, 91, 92-101. · Zbl 1468.62238
[173] Zhao, Y., Su, Y., & Yang, H. (2020). Jackknife empirical likelihood inference for the Pietra ratio. Computational Statistics and Data Analysis, 152, 1-18. · Zbl 1510.62214
[174] Zhao, Y., & Yang, S. (2012). Empirical likelihood confidence intervals for regression parameters of the survival rate. Journal of Nonparametric Statistics, 24, 59-70. · Zbl 1416.62629
[175] Zheng, M., Zhao, Z., & Yu, W. (2012). Empirical likelihood methods based on influence functions. Statistics and its Interface, 5, 355-366. · Zbl 1383.62138
[176] Zhong, P.‐S., & Chen, S. (2014). Jackknife empirical likelihood inference with regression imputation and survey data. Journal of Multivariate Analysis, 129, 193-205. · Zbl 1360.62148
[177] Zhong, X., & Ghosh, M. (2016). Higher‐order properties of Bayesian empirical likelihood. Electronic Journal of Statistics, 10, 3011-3044. · Zbl 1358.62037
[178] Zhou, M. (2015). Empirical likelihood method in survival analysis. Chapman and Hall/CRC.
[179] Zhou, M., & Yang, Y. (2015). A recursive formula for the Kaplan-Meier estimator with mean constraints and its application to empirical likelihood. Computational Statistics, 30, 1097-1109. · Zbl 1329.65038
[180] Zhou, W., & Jing, B.‐Y. (2003a). Adjusted empirical likelihood method for quantiles. Annals of the Institute of Statistical Mathematics, 55, 689-703. · Zbl 1047.62041
[181] Zhou, W., & Jing, B.‐Y. (2003b). Smoothed empirical likelihood confidence intervals for the difference of quantiles. Statistica Sinica, 13, 83-95. · Zbl 1017.62030
[182] Zhu, L., Lin, L., Cui, X., & Li, G. (2010). Bias‐corrected empirical likelihood in a multi‐link semiparametric model. Journal of Multivariate Analysis, 101, 850-868. · Zbl 1181.62039
[183] Zhu, L., & Xue, L. (2006). Empirical likelihood confidence regions in a partially linear single‐index model. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68, 549-570. · Zbl 1110.62055
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