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	<title>Semangat Maju Terus Pantang MACET</title>
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		<title>Semangat Maju Terus Pantang MACET</title>
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		<item>
		<title>GASING</title>
		<link>http://dhin.wordpress.com/2011/11/16/gasing/</link>
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		<pubDate>Wed, 16 Nov 2011 23:47:21 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Matematika]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=359</guid>
		<description><![CDATA[Bagaimana belajar matematika dengan Gampang Asyik dan menyenangkan ala Prof.Yohanes Surya, Ph.D! &#160;<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=359&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>Model LINIER</title>
		<link>http://dhin.wordpress.com/2009/04/27/model-linier/</link>
		<comments>http://dhin.wordpress.com/2009/04/27/model-linier/#comments</comments>
		<pubDate>Mon, 27 Apr 2009 02:47:29 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Model Linier]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=340</guid>
		<description><![CDATA[MODEL LINIER 1. BENTUK KUADRATIK DAN DISTRIBUSINYA 2. Turunan Bentuk Kuadrat, Ekspektasi, Variansi Vektor dan Matriks 3. NILAI EKSPEKTASI DARI VEKTOR RANDOM 4. Distribusi beberapa bentuk kuadratik khusus 5. INDEPENDEN OF QUADRATIC FORM 6. DERAJAT PENUH 7. ESTIMATOR KUADRAT MINIMUM DARI MODEL PARAMETER 8. ESTIMASI VARIANS 9. MAXIMUM LIKELIHOOD ESTIMATORS ( ADVANCES ) ESTIMATOR KEMUNGKINAN [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=340&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>Applied Nonparametric Regression</title>
		<link>http://dhin.wordpress.com/2009/04/27/applied-nonparametric-regression/</link>
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		<pubDate>Mon, 27 Apr 2009 01:24:26 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[NonParametrik Regression]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=337</guid>
		<description><![CDATA[Applied Nonparametric Regression Wolfgang Hardle Humboldt-Universitat zu Berlin Wirtschaftswissenschaftliche Fakultat Institut fur Statistik und Okonometrie Spandauer Str. 1 D{10178 Berlin1994} Free Download Applied Nonparametric Regression<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=337&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>Nonparametric Bayesian Data Analysis.</title>
		<link>http://dhin.wordpress.com/2009/04/27/nonparametric-bayesian-data-analysis/</link>
		<comments>http://dhin.wordpress.com/2009/04/27/nonparametric-bayesian-data-analysis/#comments</comments>
		<pubDate>Mon, 27 Apr 2009 01:15:58 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[NonParametrik Regression]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=333</guid>
		<description><![CDATA[Nonparametric Bayesian Data Analysis. Peter Muller Fernando A. Quintana Abstract We review the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation. For each inference problem we review relevant nonparametric Bayesian models and approaches including Dirichlet process [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=333&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>Kajian Teori Regresi Parametrik Normal dan Regresi Non Parametrik  (Theory Presentation of Normal Parametric Regression and  Nonparametric Regression)</title>
		<link>http://dhin.wordpress.com/2009/04/27/kajian-teori-regresi-parametrik-normal-dan-regresi-non-parametrik-theory-presentation-of-normal-parametric-regression-and-nonparametric-regression/</link>
		<comments>http://dhin.wordpress.com/2009/04/27/kajian-teori-regresi-parametrik-normal-dan-regresi-non-parametrik-theory-presentation-of-normal-parametric-regression-and-nonparametric-regression/#comments</comments>
		<pubDate>Mon, 27 Apr 2009 01:07:38 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[NonParametrik Regression]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=330</guid>
		<description><![CDATA[Kajian Teori Regresi Parametrik Normal dan Regresi Non Parametrik (Theory Presentation of Normal Parametric Regression and Nonparametric Regression) Yulia, S1, IM Tirta2 dan Rita Ratih T2 1Mahasiswa Jurusan Matematika FMIPA Universitas Jember 2Staf Pengajar Jurusan Matematika FMIPA Universitas Jember ABSTRAK In this paper, observation of the analysis normal parametric regression by least square method and [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=330&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>About Regression Estimators  with High Breakdown Point</title>
		<link>http://dhin.wordpress.com/2009/04/26/about-regression-estimators-with-high-breakdown-point/</link>
		<comments>http://dhin.wordpress.com/2009/04/26/about-regression-estimators-with-high-breakdown-point/#comments</comments>
		<pubDate>Sun, 26 Apr 2009 17:17:54 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Breakdown point]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=327</guid>
		<description><![CDATA[About Regression Estimators with High Breakdown Point Vandev, D.L. Inst. of Mathematics., Bulg. Acad. Sciences, P.O.Box 373,1113 Sofia, Bulgaria and Neykov, N.M. 1 Inst. of Meteorology and Hydrology, Bulg. Acad.Sciences, 66 Tsarigradsko chaussee, 1784 Sofia, Bulgaria Abstract A generalisation of a theorem by Vandev (1993) concerning the finite sample breakdown point is given. Using this [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=327&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>High Breakdown Point Multivariate M-Estimation</title>
		<link>http://dhin.wordpress.com/2009/04/26/high-breakdown-point-multivariate-m-estimation/</link>
		<comments>http://dhin.wordpress.com/2009/04/26/high-breakdown-point-multivariate-m-estimation/#comments</comments>
		<pubDate>Sun, 26 Apr 2009 17:15:21 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Breakdown point]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=324</guid>
		<description><![CDATA[High Breakdown Point Multivariate M-Estimation D. E. Tyler 1 Department of Statistics Hill Center, Busch Campus Rutgers, The State University of New Jersey 110 Frelinghuysen Road Piscataway, NJ 08854-8019 Abstract: In this talk, a general study of the properties of the M-estimates of multivariate location and scatter with auxiliary scale proposed in Tatsuoka and Tyler [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=324&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>High Breakdown Estimation Methods for  Phase I Multivariate Control Charts</title>
		<link>http://dhin.wordpress.com/2009/04/26/high-breakdown-estimation-methods-for-phase-i-multivariate-control-charts/</link>
		<comments>http://dhin.wordpress.com/2009/04/26/high-breakdown-estimation-methods-for-phase-i-multivariate-control-charts/#comments</comments>
		<pubDate>Sun, 26 Apr 2009 17:12:59 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Breakdown point]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=322</guid>
		<description><![CDATA[High Breakdown Estimation Methods for Phase I Multivariate Control Charts Willis A. Jensen, Jeffrey B. Birch, and William H. Woodall Abstract The goal of Phase I monitoring of multivariate data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=322&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>Fast algorithms fo r computing high breakdown   covariance matrices with missing data</title>
		<link>http://dhin.wordpress.com/2009/04/26/fast-algorithms-fo-r-computing-high-breakdown-covariance-matrices-with-missing-data/</link>
		<comments>http://dhin.wordpress.com/2009/04/26/fast-algorithms-fo-r-computing-high-breakdown-covariance-matrices-with-missing-data/#comments</comments>
		<pubDate>Sun, 26 Apr 2009 17:10:28 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Breakdown point]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=320</guid>
		<description><![CDATA[Fast algorithms fo r computing high breakdown covariance matrices with missing data Samuel Copt and Maria-Pia Victoria-Feser No 2003.04 Abstract Robust estimation of covariance matrices when some of the data at hand are missing is an important problem. It has been studied by Little and Smith (1987) and more recently by Cheng and Victoria-Feser (2002). [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=320&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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		<title>Breakdown points of trimmed likelihood estimators and related estimators   in generalized linear models</title>
		<link>http://dhin.wordpress.com/2009/04/26/breakdown-points-of-trimmed-likelihood-estimators-and-related-estimators-in-generalized-linear-models/</link>
		<comments>http://dhin.wordpress.com/2009/04/26/breakdown-points-of-trimmed-likelihood-estimators-and-related-estimators-in-generalized-linear-models/#comments</comments>
		<pubDate>Sun, 26 Apr 2009 17:06:13 +0000</pubDate>
		<dc:creator>dhin</dc:creator>
				<category><![CDATA[Breakdown point]]></category>

		<guid isPermaLink="false">http://dhin.wordpress.com/?p=317</guid>
		<description><![CDATA[Breakdown points of trimmed likelihood estimators and related estimators in generalized linear models By CHRISTINE H. MULLER and NEYKO NEYKOV March 2001 Summary Lower bounds for breakdown points of trimmed likelihood (TL) estimators in a general setup are expressed by the fullness parameter of Vandev (1993), where results of Vandev and Neykov (1998) are extended. [...]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=dhin.wordpress.com&amp;blog=1181125&amp;post=317&amp;subd=dhin&amp;ref=&amp;feed=1" width="1" height="1" />]]></description>
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