3—analysis of strength data p 214r 3

... sample of n ˆ 30 from population of N ˆ 39 3 will produce a likelihood ratio of or more supporting population proportion p over the true value, 100a3 93 ˆ 0X254 66 INTERPRETING A SAMPLE AS EVIDENCE ... A POPULATION represents evidence supporting p2 ˆ 101a3 93 over p1 ˆ 100a3 93 by a factor as large as This is true for all values of p2 from 101 /39 3 through 106 /39 3 For the next larger value, p2 ... likelihood for population mean under Poisson model Sample of n ˆ 30 hospitals with mean 8 03. 4 patients/hospital 68 INTERPRETING A SAMPLE AS EVIDENCE ABOUT A POPULATION The profile likelihood, Lp ("U...

Ngày tải lên: 14/08/2014, 09:20

... 0 0 B 03 B 13 0 PD04 PD14 PD05 PD15 PD06 PD16 PD07 C01 PD08 C02 0 0 0 0 B04 B14 B05 B15 PD09 C 03 PD10 C04 Figure 1-4 Part of a CANOCO data file in the full format The hyphens in the first data line ... with the specification of the so-called full format 15 WCanoImp produced data (I5,1X,21F3.0) 21 -1 1 0 1 48 1 0 0 0 0 PhosphatBenlate Year94 B06 B07 B08 B16 PD01 PD02 PD 03 PD11 PD12 PD 13 file ... the simple scattergrams of ordination scores and providing appropriate mutual rescaling of scores when preparing so-called biplots and triplots, CanoDraw enables further exploration of our data...

Ngày tải lên: 29/03/2014, 17:20

Ngày tải lên: 17/04/2014, 09:16

Ngày tải lên: 14/08/2014, 08:21

... F Smith: Publications up to 2002 Author Index Subject Index 31 8 31 9 32 3 32 3 32 6 32 8 33 4 33 8 33 9 34 3 34 5 36 1 36 7 37 3 Preface The book is dedicated to T M F (Fred) Smith, and marks his `official' ... 294 297 297 297 30 3 30 6 30 7 30 7 30 8 30 8 30 9 31 0 31 2 31 3 31 5 xiv CONTENTS 19.4 19.5 Chapter 20 Imputation procedures Example Analysis Combining Survey and Geographically Aggregated Data D G Steel, ... 23 26 27 29 29 31 32 32 33 viii CONTENTS 3. 3 3. 4 3. 5 3. 6 3. 7 Chapter Chapter Design-based and total variances of linear estimators 3. 3.1 Design-based and total variance of ” b ” 3. 3.2 Design-based...

Ngày tải lên: 14/08/2014, 09:20

... that Ep (It dt ) ˆ 1aN o(N À1 ), (3X12) so that ^ˆb b 3. 2 .3 op (1)X (3X 13) ^ Properties of b and ^ b ^ Let us now consider ^ and b from the perspective of estimating b It follows b from (3. 13) that ... Chapter 3, one can develop an analytic perspective that justifies the pseudo-likelihood approach This perspective takes account of the total variation from both the population generating process ... Example Two-stage sampling Suppose the population is divided into C clusters, based for example on geographical areas A simple form of two-stage sampling first selects a simple random sample of...

Ngày tải lên: 14/08/2014, 09:20

... obtained by replacing b with bl in pi (x) (i.e ~1 (x) p p p p represents the smoothed sample proportion, and p1 (x) the fitted population proportion, of cases at x) These weights are plotted at ... Sample Design Figure 8.1 Approximations to population curve we have plotted two linear approximations The solid line corresponds to B, the `whole-population approximation' The dashed-line approximation ... vector of bin p p midpoints and ^ ˆ ( ^1 , ^2 , F F F , ^I ) is the vector of survey estimates of p p p bin proportions When binning has been carried out to the level of precision of the data, ...

Ngày tải lên: 14/08/2014, 09:20

... 1. 83 À0 .31 2.09 À0 .36 0.40 0. 13 2. 13 1.86 À0 .35 2. 23 À0 .38 0.64 0.17 2 .38 1.91 À0.17 2 .33 À0.18 0.84 0.25 2.56 1.99 Average root mean squared error M(Ps ) 3. 80 14.08 M(Zs Ps ) M(Z) 3. 36 Elin(Ps ... (wjp) (11X1) fU (p) ˆ fs (p) EU (P) ap ˆ fs (p) a(pEs (1aP))X (11X2) and Consequently ‚ fU (w) ˆ and so p 1 fs (wjp) fs (p) dp  à Es P 1 (11X3)  à  à EU (W ) ˆ Es P 1 Es (W jP) aEs P 1 X Recollect ... The two sampling procedures we used for this population were based on inclusion probabilities that were approximately proportional to the population values of Z (PPZ: an informative sampling method)...

Ngày tải lên: 14/08/2014, 09:20

... are appropriate for longitudinal analysis based upon data for the attrition sample st of individuals who respond up to wave t (Lepkowski, 1989) Thus, the simplest approach is to use only data ... allow for complex sampling schemes 13. 3 DISCRETE RESPONSE DATA discrete response data We now turn to the case when the response variable Y is discrete One approach to the analysis of such data is ... approach of Pfeffermann et al (1998) The two approaches may be compared in a number of ways: The multilevel approach incorporates the different attrition samples more directly, although the possible...

Ngày tải lên: 14/08/2014, 09:20

... sample design information and denote Sz (t) ˆ Pr(Ti ! tjZi ˆ z) Assume for simplicity that Z is discrete and let Pz ˆ P( Zi ˆ z) and p( z) ˆ Pr(Ii ˆ 1jZi ˆ z); note that Pz is part of the superpopulation ... a YTS episode, exp (wp– y– uYTS ) Pr(r ˆ ljd ˆ 2, x, u) ˆ € yp uYTS ) p exp (wpp (16X11) where w is a vector of relevant covariates In the sample there are no cases at all of a college spell following ... individual event history processes Problems of analysis can arise, however, because of the complexity of the populations and processes being studied, the use of complex sampling designs, and limitations...

Ngày tải lên: 14/08/2014, 09:20

... let Njk be the population size and P ˆ (P1 1 , F F F , PJK ) where Pjk ˆ NjkaN is the population proportion Also, let njk and pjk ˆ njkan denote the number and proportion of sampled units and ... following example provides the main idea Example sample Unit nonresponse adjustments for the mean from a simple random Suppose a simple random sample of size n is selected from a population of size ... estimators of b0 and g are based on the phase sample data, while that of f is the phase "U sample mean of D When these are substituted into the expression for ^opt in y the preceding paragraph it...

Ngày tải lên: 14/08/2014, 09:20

... )nÀ1 S , where ^ S2 ˆ ˆ tPs2 and yp ˆ ˆ tPs2 3 1 p 1 p 1 1t 2tjs ˆ tPs2 3 1 p 1 p 1 1t 2tjs ˆ tPs2 p 1 p 1 ( yt À yp )2 1t 2tjs p 1 p 1 yt X 1t 2tjs A general estimator of V { y1 } can be constructed ... uPs2 Now E @ ˆ uPs2 ˆ tPs2 A p 1 ert js2 1t p 1 eu 1u À E tPs1 ’sc p 1 )js1 2ujs W a p 1 e js XtPs ’sc 1t rt Y ˆ ˆ À1 p1 t Bu eu X A p 1 p 1 (1 1u 2ujs V ` ˆ ˆ uPs2 ˆ uPs1 ˆE (19X32) p 1 (1 À p 1 ... SD 1 5 m0 ˆ 37 1, n0 ˆ 18 880 m0 ˆ 37 1, n0 ˆ 37 76 n0 ˆ 37 76, m1 ˆ 37 1 n0 ˆ 37 8, m1 ˆ 37 1 n0 ˆ 38 , m1 ˆ 37 1 m1 ˆ 37 1 22.8 22.7 22.7 23. 0 21.9 29.2 0. 637 1. 536 1.502 4.910 13. 132 43. 361 1.81 1.72...

Ngày tải lên: 14/08/2014, 09:20

... Valuation, 23, 30 31 , 35 , 37 , 80, 120, 125 Value book, 18–20, 29 company, 24, 29 drivers of, 35 39 , 134 ending, 33 , 35 , 44, 48, 149–150, 154 market, 18–20, 42 organization, 19, 37 38 , 40, 43 44, 79, ... long-range, 53, 55 meeting, 55, 79, 81, 83 strategic, 53, 55, 56 workshop, 159–160 Profit margin, 31 , 37 , 46, 90, 120–121, 130 , 134 , 136 – 138 , 140, 146, 152 Profitability, 16, 101 Rate of return, ... 46, 122, 146, 1 53 Capital, working, 28, 31 , 34 , 39 , 46, 122, 142, 144, 146 Cash flow discounted, 30 , 132 , 162 operating, 33 , 35 positive, 28, 48 projected, 47, 122, 1 23 Debt, cost of, 40–42, 148...

Ngày tải lên: 14/08/2014, 09:20

... 488.9 522.1 460.4 433 .4 34 7.8 425 .3 336 .8 494.7 35 7.6 36 0.8 400.1 8 53. 6 715.5 3 067 .3 21 700.0 39 0.7 457 .3 23 682.0 38 3.2 691.1 428 .3 À481.9 027.0 434 .3 958.0 0.880 833 .9 430 .0 895.8 284.5 287.9 ... 1 73 Steel, D G 278, 287, 32 3, 32 8, 32 9, 33 0, 33 1, 33 8, 33 9, 34 0, 34 1, 34 2 Stefanski, L A 229, 242 Stern, H S 52, 54, 290 Stewart, M B 2 53 Stolzenberg, R M 30 5 Stukel, D M 296 Sugden, R A 30 , 31 , ... 14, 18, 22, 23, 49, 51, 85, 86, 87, 89, 91, 104, 151, 175, 181, 207, 208, 209, 210, 2 13, 214, 229, 231 , 241, 242, 292, 32 3, 32 5, 32 7, 32 9, 33 0, 33 8, 33 9, 34 0, 34 1, 34 2 Holubkov, R 1 13 Horvitz,...

Ngày tải lên: 14/08/2014, 09:20

... already been proposed to analyse this kind of data The most commonly used at present are random regression models [3] , but other approaches focus more specically on the modelling of the covariance ... Zimmerman [ 13] propose using a parametric function for innovation variances g (t) with, for example, a polynomial of time This allows to considerably reduce the number of parameters compared to ... [14] Pletcher S.D., Geyer C.J., The genetic analysis of age-dependent traits: modelling a character process, Genetics 1 53 (1999) 825 833 [15] Pletcher S.D., Houle D., Curtsinger J.W., Age-specic properties...

Ngày tải lên: 14/08/2014, 13:22

... relationship matrix Bounds were set to large values to avoid truncation of parameter space The joint prior had the form: 2 2 p( YM, b, u, p, σa , p |hyper-parameters) = p( YM )p( b )p( u )p( σa )p( p ) 461 ... (NMD): 30 0 NMDi = j=14 [1 − Φ(µij )] = 287 − MDi Longitudinal binary data 465 (e) Probability of no mastitis at 30 , 150 and 30 0 days: p( yi30 = 0, yi150 = 0, yi300 = 0) = pi30 (0)pi150 (0)pi300 ... data Table I Distribution of cows by the number of mastitis cases Test with mastitis 10 Number of cows 270 32 11 3 1 1 % of total 82.1 9.4 3. 3 2.1 0.9 0.0 0.9 0 .3 0 .3 0 .3 0 .3 2.2 Methods 2.2.1 Cross-sectional...

Ngày tải lên: 14/08/2014, 13:22

... feature space A.2 Box plot of testing example 31 28 along iteration of MCMC samplings A .3 Box plot of testing example 864 along iteration of MCMC samplings A.4 Box plot of testing example 37 52 ... iteration of MCMC samplings A.5 Box plot of testing example 1171 along iteration of MCMC samplings A.6 Box plot of testing example 139 along iteration of MCMC samplings A.7 Box plot of testing example ... 20 24 24 26 27 29 29 30 30 31 31 32 32 32 33 35 38 39 iv Methodology using Gaussian Processes 43 6.1 Feature Selection 43 6.2 Unbiased Test Accuracy...

Ngày tải lên: 28/09/2015, 13:38

... whether discrepancy exists between the asymptotical properties and the finite sample size performance in GEE method CHAPTER IMPLICATION OF MISSPECIFICATION 32 Chapter Implication of Misspecification ... dispersion parameter Probability distributions of response Y in GLM are usually parameterized in terms of the mean µ and the dispersion parameter φ instead of the natural parameter θ For example, ... 19 3. 2 GEE Approach 20 3. 3 Estimation of Correlation Parameters 21 3. 3.1 Moment Method (MOM) 22 3. 3.2 Gaussian Method...

Ngày tải lên: 05/10/2015, 13:53

... 36 3. 3.2 Estimation of Regression Parameters β 37 3. 3 .3 Estimation of Variance Parameter γ 37 3. 3.4 Estimation of Correlation Parameters α 38 ... assigned to placebo (0) and subjects assigned to progabide (1) Period subject Trt Baseline Age 3 11 31 3 11 30 25 4 4 26 18 21 66 22 29 11 14 76 18 30 38 32 31 19 20 32 10 30 33 19 18 The data for ... 31 3. 2.4 Estimation of Scale Parameter φ 34 3. 2.5 Iterative Computation 35 3. 3 Parameter Estimation For Longitudinal Data 36 3. 3.1 Preview...

Ngày tải lên: 05/10/2015, 19:02

... 51 3. 3.2 Epidemiological data pre-processing 57 3. 3 .3 Clustering analysis of epidemiological data 60 3. 3.4 Geo-processing of epidemiology data analysis 63 3.4 Summary ... geospatial visualisation 1 .3. 3 WebEpi WebEpi consists of data pre-processing, data clustering and data Geoprocessing The function of data pre-processing is to convert and re-structure DHHS epidemiological ... 135 7.1.1 Clustering analysis of epidemiological data 136 7.1.2 Geospatial visualisation of epidemiological data 137 7.1 .3 WebEpi 138 7.2 Conclusions 138 7.3...

Ngày tải lên: 04/12/2015, 14:00