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Systematic Statistics Used for Data Compression in Space Telemetry
The need for data compression, a consequence of the demands made on the telemetry system of a space vehicle, prompts consideration of the use of sample quantiles in estimating population parameters and obtaining tests of goodness of fit for large samples. In this paper optimal unbiased estimators of...
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Published in: | Journal of the American Statistical Association 1965-03, Vol.60 (309), p.97-133 |
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creator | Eisenberger, Isidore Posner, Edward C. |
description | The need for data compression, a consequence of the demands made on the telemetry system of a space vehicle, prompts consideration of the use of sample quantiles in estimating population parameters and obtaining tests of goodness of fit for large samples. In this paper optimal unbiased estimators of the mean and standard deviation are given using up to twenty quantiles when the parent population is normal. Moreover, the estimators are relatively insensitive to deviations from normality. A distribution-free goodness-of-fit test is presented based on the sum of the squares of four quantiles after an orthogonal transformation to independent normal deviates. If a frequency function is of the form f(x; p) = pf
1
(x) + (1 - p) f
2
(x), 0 < p < 1, where f
1
and f
2
are normal frequency functions, the distribution is likely to be bimodal. Another goodness-of-fit test is obtained using four quantiles, which is likely to have considerable power with a null hypothesis of normality and the alternative hypothesis of bimodality. The "data compression ratios" obtained with the use of a quantile system can be on the order of 100 to 1. |
doi_str_mv | 10.1080/01621459.1965.10480778 |
format | article |
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1
(x) + (1 - p) f
2
(x), 0 < p < 1, where f
1
and f
2
are normal frequency functions, the distribution is likely to be bimodal. Another goodness-of-fit test is obtained using four quantiles, which is likely to have considerable power with a null hypothesis of normality and the alternative hypothesis of bimodality. The "data compression ratios" obtained with the use of a quantile system can be on the order of 100 to 1.</description><identifier>ISSN: 0162-1459</identifier><identifier>EISSN: 1537-274X</identifier><identifier>DOI: 10.1080/01621459.1965.10480778</identifier><language>eng</language><publisher>Washington: Taylor & Francis Group</publisher><subject>Data compression ; Density distributions ; Estimators ; Estimators for the mean ; Gaussian distributions ; Population distributions ; Quantum efficiency ; Standard deviation ; Statistical discrepancies ; Telemetry</subject><ispartof>Journal of the American Statistical Association, 1965-03, Vol.60 (309), p.97-133</ispartof><rights>Copyright Taylor & Francis Group, LLC 1965</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-49bbea6de718e5d988f3a455b6339ed3c2f05cd8a2386705e4129c2be2e4a5213</citedby><cites>FETCH-LOGICAL-c349t-49bbea6de718e5d988f3a455b6339ed3c2f05cd8a2386705e4129c2be2e4a5213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2283140$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2283140$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,27900,27901,58212,58445</link.rule.ids></links><search><creatorcontrib>Eisenberger, Isidore</creatorcontrib><creatorcontrib>Posner, Edward C.</creatorcontrib><title>Systematic Statistics Used for Data Compression in Space Telemetry</title><title>Journal of the American Statistical Association</title><description>The need for data compression, a consequence of the demands made on the telemetry system of a space vehicle, prompts consideration of the use of sample quantiles in estimating population parameters and obtaining tests of goodness of fit for large samples. In this paper optimal unbiased estimators of the mean and standard deviation are given using up to twenty quantiles when the parent population is normal. Moreover, the estimators are relatively insensitive to deviations from normality. A distribution-free goodness-of-fit test is presented based on the sum of the squares of four quantiles after an orthogonal transformation to independent normal deviates. If a frequency function is of the form f(x; p) = pf
1
(x) + (1 - p) f
2
(x), 0 < p < 1, where f
1
and f
2
are normal frequency functions, the distribution is likely to be bimodal. Another goodness-of-fit test is obtained using four quantiles, which is likely to have considerable power with a null hypothesis of normality and the alternative hypothesis of bimodality. The "data compression ratios" obtained with the use of a quantile system can be on the order of 100 to 1.</description><subject>Data compression</subject><subject>Density distributions</subject><subject>Estimators</subject><subject>Estimators for the mean</subject><subject>Gaussian distributions</subject><subject>Population distributions</subject><subject>Quantum efficiency</subject><subject>Standard deviation</subject><subject>Statistical discrepancies</subject><subject>Telemetry</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1965</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LAzEQhoMoWKt_QRb0ujWfm-RY6ycUPLQFbyG7Owtbdjc1SZH-e1PWgjfnMsPwvDPwIHRL8IxghR8wKSjhQs-ILkRacYWlVGdoQgSTOZX88xxNjlB-pC7RVQhbnEoqNUGPq0OI0NvYVtkqphbSFLJNgDprnM-ebLTZwvU7DyG0bsjaIVvtbAXZGjroIfrDNbpobBfg5rdP0ebleb14y5cfr--L-TKvGNcx57oswRY1SKJA1FqphlkuRFkwpqFmFW2wqGplKVOFxAI4obqiJVDgVlDCpuhuvLvz7msPIZqt2_shvTSEESJ1wYlOVDFSlXcheGjMzre99QdDsDn6Midf5ujLnHyl4P0Y3Ibo_N8UZVgaShUjHCdsPmLtkPz09tv5rjbRHjrnG2-Hqg2G_fPqB9xzfJw</recordid><startdate>19650301</startdate><enddate>19650301</enddate><creator>Eisenberger, Isidore</creator><creator>Posner, Edward C.</creator><general>Taylor & Francis Group</general><general>American Statistical Association</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JRZRW</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</scope></search><sort><creationdate>19650301</creationdate><title>Systematic Statistics Used for Data Compression in Space Telemetry</title><author>Eisenberger, Isidore ; Posner, Edward C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-49bbea6de718e5d988f3a455b6339ed3c2f05cd8a2386705e4129c2be2e4a5213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1965</creationdate><topic>Data compression</topic><topic>Density distributions</topic><topic>Estimators</topic><topic>Estimators for the mean</topic><topic>Gaussian distributions</topic><topic>Population distributions</topic><topic>Quantum efficiency</topic><topic>Standard deviation</topic><topic>Statistical discrepancies</topic><topic>Telemetry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eisenberger, Isidore</creatorcontrib><creatorcontrib>Posner, Edward C.</creatorcontrib><collection>CrossRef</collection><collection>Periodicals Index Online Segment 35</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - West</collection><collection>Primary Sources Access (Plan D) - International</collection><collection>Primary Sources Access & Build (Plan A) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Midwest</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Northeast</collection><collection>Primary Sources Access (Plan D) - Southeast</collection><collection>Primary Sources Access (Plan D) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Southeast</collection><collection>Primary Sources Access (Plan D) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - UK / I</collection><collection>Primary Sources Access (Plan D) - Canada</collection><collection>Primary Sources Access (Plan D) - EMEALA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - International</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - International</collection><collection>Primary Sources Access (Plan D) - West</collection><collection>Periodicals Index Online Segments 1-50</collection><collection>Primary Sources Access (Plan D) - APAC</collection><collection>Primary Sources Access (Plan D) - Midwest</collection><collection>Primary Sources Access (Plan D) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Canada</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - EMEALA</collection><collection>Primary Sources Access & Build (Plan A) - APAC</collection><collection>Primary Sources Access & Build (Plan A) - Canada</collection><collection>Primary Sources Access & Build (Plan A) - West</collection><collection>Primary Sources Access & Build (Plan A) - EMEALA</collection><collection>Primary Sources Access (Plan D) - Northeast</collection><collection>Primary Sources Access & Build (Plan A) - Midwest</collection><collection>Primary Sources Access & Build (Plan A) - North Central</collection><collection>Primary Sources Access & Build (Plan A) - Northeast</collection><collection>Primary Sources Access & Build (Plan A) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - Southeast</collection><collection>Primary Sources Access (Plan D) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - APAC</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - MEA</collection><jtitle>Journal of the American Statistical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eisenberger, Isidore</au><au>Posner, Edward C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Systematic Statistics Used for Data Compression in Space Telemetry</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>1965-03-01</date><risdate>1965</risdate><volume>60</volume><issue>309</issue><spage>97</spage><epage>133</epage><pages>97-133</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><abstract>The need for data compression, a consequence of the demands made on the telemetry system of a space vehicle, prompts consideration of the use of sample quantiles in estimating population parameters and obtaining tests of goodness of fit for large samples. In this paper optimal unbiased estimators of the mean and standard deviation are given using up to twenty quantiles when the parent population is normal. Moreover, the estimators are relatively insensitive to deviations from normality. A distribution-free goodness-of-fit test is presented based on the sum of the squares of four quantiles after an orthogonal transformation to independent normal deviates. If a frequency function is of the form f(x; p) = pf
1
(x) + (1 - p) f
2
(x), 0 < p < 1, where f
1
and f
2
are normal frequency functions, the distribution is likely to be bimodal. Another goodness-of-fit test is obtained using four quantiles, which is likely to have considerable power with a null hypothesis of normality and the alternative hypothesis of bimodality. The "data compression ratios" obtained with the use of a quantile system can be on the order of 100 to 1.</abstract><cop>Washington</cop><pub>Taylor & Francis Group</pub><doi>10.1080/01621459.1965.10480778</doi><tpages>37</tpages></addata></record> |
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source | JSTOR Archival Journals and Primary Sources Collection |
subjects | Data compression Density distributions Estimators Estimators for the mean Gaussian distributions Population distributions Quantum efficiency Standard deviation Statistical discrepancies Telemetry |
title | Systematic Statistics Used for Data Compression in Space Telemetry |
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