<html><head></head><body><div style="color:#000; background-color:#fff; font-family:times new roman, new york, times, serif;font-size:16px"><h1 style="border: 0px; font-family: Georgia, 'New Century Schoolbook', 'Nimbus Roman No9 L', serif; font-size: 3rem; font-weight: normal; line-height: 1.2; margin-top: 0px; margin-bottom: 0.5em; padding: 0px; vertical-align: baseline; letter-spacing: 0.02em; color: rgb(51, 51, 51);" id="yui_3_16_0_ym19_1_1464379218160_13015"><a href="http://www.springer.com/us/book/9780387759586" id="yui_3_16_0_ym19_1_1464379218160_13056" style="font-family: 'Helvetica Neue', Arial, Helvetica, sans-serif; font-size: 13px;">Time Series Analysis - With Applications in R | Jonathan D. Cryer | Springer</a><br></h1><div style="width:450px; font-family: 'Georgia', 'Times', 'Times New Roman', 'serif';margin-top:5px; margin-bottom: 5px; background-color: #ffffff;" contenteditable="false" id="enhancrCard_0" class="link-enhancr-attachment link-enhancr-element richcompose-card"><table class="link-enhancr-element" cellspacing="0" cellpadding="0" border="0" style="width:450px; height:auto; position: relative; display: block;" id="yui_3_16_0_ym19_1_1464379218160_13121"><tbody id="yui_3_16_0_ym19_1_1464379218160_13120"><tr class="link-enhancr-element"><td class="link-enhancr-element" colspan="7" style="height: 1px; background-color: #e5e5e5; font-size: 1px; border-collapse: collapse;"><div class="link-enhancr-element" style="height: 1px; background-color: #e5e5e5; font-size: 1px; line-height:0px;"> </div></td></tr><tr class="link-enhancr-element"><td rowspan="5" class="link-enhancr-element" style="width: 1px; background-color: #e5e5e5; font-size: 1pt; border-collapse: collapse;"><div class="link-enhancr-element" style="width: 1px; background-color: #e5e5e5; font-size: 1pt;"> </div></td><td rowspan="5" class="link-enhancr-element" style="width: 14px; background-color: #ffffff; font-size: 0pt; border-collapse: collapse;"><div class="link-enhancr-element" style="width: 14px; background-color: #ffffff; font-size: 14pt;"> </div></td><td colspan="2" class="link-enhancr-element" style="height: 6px; background-color: #ffffff; font-size: 0pt; border-collapse: collapse;"><div class="link-enhancr-element" style="height: 6px; background-color: #ffffff; font-size: 6pt;"> </div></td><td rowspan="5" class="link-enhancr-element" style="width: 20px; background-color: #ffffff; font-size: 0pt; border-collapse: collapse;"><div class="link-enhancr-element" style="width: 20px; background-color: #ffffff; font-size: 20pt;"> </div></td><td class="link-enhancr-element" rowspan="5" width="1" style="width: 1px; background-color: #e5e5e5; font-size: 1pt; border-collapse: collapse;"><div class="link-enhancr-element" style="width: 1px; background-color: #e5e5e5; font-size: 1pt;"> </div></td></tr><tr id="yui_3_16_0_ym19_1_1464379218160_13119"><td class="link-enhancr-element" colspan="2" style="width: 100%; vertical-align: middle; font-family: 'Georgia', 'Times', 'Times New Roman', 'serif';" id="yui_3_16_0_ym19_1_1464379218160_13118"><div class="link-enhancr-text-part link-enhancr-element" style="line-height:16.5px; background-color: #ffffff; width: 414px;" id="yui_3_16_0_ym19_1_1464379218160_13117"><div class="link-enhancr-element" style="word-wrap: break-word; word-break: break-all;" id="yui_3_16_0_ym19_1_1464379218160_13116"><span class="link-enhancr-element icon icon-shrink link-enhancr-toggle"></span><span class="link-enhancr-element icon icon-close link-enhancr-delete"></span><a href="http://www.springer.com/us/book/9780387759586" class="link-enhancr-card-urlWrapper link-enhancr-element" style="text-decoration: none !important;text-decoration:none; color: #000000 !important; line-height: 100%; font-size: 18px; display: block;" id="yui_3_16_0_ym19_1_1464379218160_13115"><span class="link-enhancr-element link-enhancr-card-title" style="margin: 0; font-weight: normal;margin-bottom: 3px; font-size: 18px; line-height: 21px; max-height: 43px; color: #000000; overflow: hidden !important; display: inline-block;">Time Series Analysis - With Applications in R | Jonathan D. Cryer | Springer</span></a><div style="font-size: 13px; line-height: 20px; color: #999999; max-height: 81px; font-family: 'Georgia', 'Times', 'Times New Roman', 'serif';overflow: hidden;" class="link-enhancr-card-description link-enhancr-element" id="yui_3_16_0_ym19_1_1464379218160_13122">Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications....</div></div></div></td></tr><tr><td colspan="2" class="link-enhancr-element" style="height: 6px; background-color: #ffffff; font-size: 0pt; border-collapse: collapse;"><div class="link-enhancr-element" style="height: 6px; background-color: #ffffff; font-size: 6pt;"></div></td></tr><tr><td class="link-enhancr-element" style="vertical-align: middle; font-family: 'Arial', 'Helvetica Neue', 'Helvetica', 'sans-serif';"><div class="link-enhancr-element" style="font-size: 0pt;"><a href="http://www.springer.com/us/book/9780387759586" class="link-enhancr-card-url link-enhancr-element" style="color: black; 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text-align: right; line-height: 11px; max-height: 13px; font-size: 0pt;"><span class="link-enhancr-element link-enhancr-preview-by link-enhancr-mobile-no-resize" style="vertical-align:middle; font-size: 9px; line-height: 11px; color: #999999; -moz-text-size-adjust: none; -ms-text-size-adjust: none; -webkit-text-size-adjust:none; text-size-adjust:none;">Visualizado por Yahoo</span></div></td></tr><tr><td colspan="2" class="link-enhancr-element" style="height: 9px; background-color: #ffffff; font-size: 0pt; border-collapse: collapse;"><div class="link-enhancr-element" style="height: 9px; background-color: #ffffff; font-size: 9pt;"></div></td></tr><tr class="link-enhancr-element"><td class="link-enhancr-element" colspan="7" style="height: 1px; background-color: #e5e5e5; font-size: 1px; border-collapse: collapse;"><div class="link-enhancr-element" style="height: 1px; background-color: #e5e5e5; font-size: 1px; line-height:0px"> </div></td></tr></tbody></table></div><div id="yui_3_16_0_ym19_1_1464379218160_13017"><br></div><div></div><div id="yui_3_16_0_ym19_1_1464379218160_12983"> </div><div class="signature" id="yui_3_16_0_ym19_1_1464379218160_12984">Edson Lira <br>Estatístico<br> Manaus-Amazonas</div> <div class="qtdSeparateBR"><br><br></div><div class="yahoo_quoted" style="display: block;"> <div style="font-family: times new roman, new york, times, serif; font-size: 16px;"> <div style="font-family: HelveticaNeue, Helvetica Neue, Helvetica, Arial, Lucida Grande, sans-serif; font-size: 16px;"> <div dir="ltr"><font size="2" face="Arial"> Em Quarta-feira, 25 de Maio de 2016 16:58, Cesar Rabak <cesar.rabak@gmail.com> escreveu:<br></font></div> <br><br> <div class="y_msg_container"><div id="yiv4344656546"><div><div dir="ltr">Fernando,<div><br clear="none"></div><div>A melhor resposta à questão "<span style="font-size:12.8px;"> </span><span style="font-size:12.8px;">Faz sentido uma ordem de X em ARMA?" é responder às seguintes questões:</span></div><div><span style="font-size:12.8px;"><br clear="none"></span></div><div><span style="font-size:12.8px;">A ARMA é a melhor abordagem para a análise do fenômeno/experimento que estou realizando?</span></div><div><span style="font-size:12.8px;"><br clear="none"></span></div><div><span style="font-size:12.8px;">O modelo subjacente de uma ordem X indica que processo de geração desses dados, e esse processo faz sentido em relação ao processo físico pelo qual os dados são gerados?</span></div><div><span style="font-size:12.8px;"><br clear="none"></span></div><div><span style="font-size:12.8px;">HTH</span></div><div><span style="font-size:12.8px;">--</span></div><div><span style="font-size:12.8px;">Cesar Rabak</span></div><div><span style="font-size:12.8px;"><br clear="none"></span></div></div><div class="yiv4344656546gmail_extra"><br clear="none"><div class="yiv4344656546gmail_quote">2016-05-25 13:32 GMT-03:00 Fernando Antonio de souza <span dir="ltr"><<a rel="nofollow" shape="rect" ymailto="mailto:nandodesouza@gmail.com" target="_blank" href="mailto:nandodesouza@gmail.com">nandodesouza@gmail.com</a>></span>:<br clear="none"><blockquote class="yiv4344656546gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><div class="yiv4344656546yqt1706143790" id="yiv4344656546yqt75078"><div>Boa tarde, colegas<div><div><br clear="none"></div><div>Estou trabalhando com modelos mistos, com dados de medidas repetidas no tempo.</div><div>Ao definir a estrutura da matriz de covariância, testei a estrutura autoregressiva de ordem 1 AR1(), Moving average ARMA(p=0,q=?), onde testei para ordem de 1 a 6 (acima de 6 o modelo não convergiu). Acontece que com a ordem 6 obtive o melhor modelo (segundo anova()), embora nem todas as correlações foram resolvidas, conforme o gráfico (anexo)</div><div><br clear="none"></div><div>Ainda não me sinto confortal em trabalhar com estas estruturas. Faz sentido uma ordem de 6 em ARMA? Vocês teriam algum material que me explique o uso destas estruturas?</div><div><br clear="none"></div><div>Atenciosamente</div><div><br clear="none"></div><div><br clear="none"></div><br clear="none"><div>=======================================================================<br clear="none">Fernando Souza<br clear="none">Zootecnista, DSc. 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