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<bibitem type="J">   <ARLID>0588208</ARLID> <utime>20240903210722.7</utime><mtime>20240806235959.9</mtime>   <SCOPUS>85197920949</SCOPUS> <WOS>001259144100001</WOS>  <DOI>10.1142/S0218539324500219</DOI>           <title language="eng" primary="1">A Statistical Model of Hazard Rate Change Caused by a Random Shock</title>  <specification> <page_count>14 s.</page_count> <media_type>P</media_type> </specification>   <serial><ARLID>cav_un_epca*0297192</ARLID><ISSN>0218-5393</ISSN><title>International Journal of Reliability, Quality and Safety Engineering</title><part_num/><part_title/><volume_id>31</volume_id><volume/></serial>    <keyword>Time to failure</keyword>   <keyword>Reliability</keyword>   <keyword>Hazard rate</keyword>   <keyword>Kijima model</keyword>   <keyword>Accelerated lifetime</keyword>    <author primary="1"> <ARLID>cav_un_auth*0101227</ARLID> <name1>Volf</name1> <name2>Petr</name2> <institution>UTIA-B</institution> <full_dept language="cz">Stochastická informatika</full_dept> <full_dept language="eng">Department of Stochastic Informatics</full_dept> <department language="cz">SI</department> <department language="eng">SI</department> <full_dept>Department of Stochastic Informatics</full_dept>  <share>100</share> <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>   <source> <url>https://www.worldscientific.com/doi/epdf/10.1142/S0218539324500219</url>  </source>        <cas_special>  <abstract language="eng" primary="1">In the framework of statistical reliability analysis, we propose a model describing the change of reliability of a technical component after certain event, as for instance a shock, or the failure and maintenance. Two kinds of failure rate changes are considered, namely a shift of virtual age of analyzed component and the acceleration of its subjective time. The objective is to estimate both the baseline lifetime probability distribution and the magnitude of these changes. The estimation procedure is based on the maximum likelihood method, both parametric and nonparametric distributions of lifetime are considered. Further, a graphical method of the goodness-of-fit test is proposed. The method is then checked on artificial examples, finally a real data problem is solved.</abstract>     <result_subspec>WOS</result_subspec> <RIV>BB</RIV> <FORD0>10000</FORD0> <FORD1>10100</FORD1> <FORD2>10102</FORD2>    <reportyear>2025</reportyear>      <num_of_auth>1</num_of_auth>  <inst_support> RVO:67985556 </inst_support>  <permalink>https://hdl.handle.net/11104/0355238</permalink>   <confidential>S</confidential>  <article_num> 2450021 </article_num> <unknown tag="mrcbC91"> C </unknown>         <unknown tag="mrcbT16-e">ENGINEERING.MULTIDISCIPLINARY</unknown> <unknown tag="mrcbT16-f">0.8</unknown> <unknown tag="mrcbT16-g">0.3</unknown> <unknown tag="mrcbT16-h">8.4</unknown> <unknown tag="mrcbT16-i">0.00014</unknown> <unknown tag="mrcbT16-j">0.091</unknown> <unknown tag="mrcbT16-k">468</unknown> <unknown tag="mrcbT16-q">34</unknown> <unknown tag="mrcbT16-s">0.25</unknown> <unknown tag="mrcbT16-y">31.19</unknown> <unknown tag="mrcbT16-x">1.49</unknown> <unknown tag="mrcbT16-3">185</unknown> <unknown tag="mrcbT16-4">Q3</unknown> <unknown tag="mrcbT16-5">0.600</unknown> <unknown tag="mrcbT16-6">57</unknown> <unknown tag="mrcbT16-7">Q3</unknown> <unknown tag="mrcbT16-C">37.2</unknown> <unknown tag="mrcbT16-M">0.28</unknown> <unknown tag="mrcbT16-N">Q3</unknown> <unknown tag="mrcbT16-P">37.2</unknown> <arlyear>2024</arlyear>       <unknown tag="mrcbU14"> 85197920949 SCOPUS </unknown> <unknown tag="mrcbU24"> PUBMED </unknown> <unknown tag="mrcbU34"> 001259144100001 WOS </unknown> <unknown tag="mrcbU63"> cav_un_epca*0297192 International Journal of Reliability, Quality and Safety Engineering 0218-5393 1793-6446 Roč. 31 č. 4 2024 </unknown> </cas_special> </bibitem>