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10.1080/10485252.2013.772178 http://scihub22266oqcxt.onion/10.1080/10485252.2013.772178 C5353374!5353374!28316457     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28316457.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Nonparametr+Stat 2013 ; 25 (2): 499-521 Nephropedia Template TP {{tp|p=28316457|t=2013. L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals |pdf=|usr=}}{{28316457}} gab.com Text L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals JO: J Nonparametr Stat http://www.kidney.de/pget.php?&tw=1&pmid=28316457 #FOAvip The L-statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L-statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L-statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation. Twit Text FOAVip L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals ????J Nonparametr Stat http://www.kidney.de/pget.php?&tw=1&pmid=28316457 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28316457 #FOAvip English Wikipedia <ref>{{Cite pmid|28316457}}</ref> L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals #MMPMID28316457 Assaad HI; Choudhary PK J Nonparametr Stat 2013[]; 25 (2): 499-521 PMID28316457show ga The L-statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L-statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L-statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation. äL-statistics for Repeated Measurements Data With Application to Trimme(epmc).pdf DeepDyve Pubget Overpricing