[精品图书]Statistics for Bioengineering Sciences

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Statistics for Bioengineering Sciences: With MATLAB and WinBUGS Support

Brani Vidakovi

Editorial ReviewsThrough its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing engineering fields, bioengineering and biomedical engineering, while implementing software that engineers are familiar with.
The author integrates introductory statistics for engineers and  introductory biostatistics as a single textbook heavily oriented to computation and hands on approaches. For example, topics ranging from the aspects of disease and device testing, Sensitivity, Specificity and ROC curves, Epidemiological Risk Theory, Survival Analysis, or Logistic and Poisson Regressions are covered.
In addition to the synergy of engineering and biostatistical approaches, the novelty of this book is in the substantial coverage of Bayesian approaches to statistical inference. Many examples in this text are solved using both the traditional and Bayesian methods, and the results are compared and commented.
http://springer.bme.gatech.edu/index.html

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Product Details Hardcover: 769 pages Publisher: Springer; 2011 edition (August 31, 2011)

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关键词：Engineering engineerin Statistics statistic Engineer software familiar growing single

1. close all
   
2. clear all
   
3. 
   
4. ImmPeroxNeg=[...
   
5. 19, 25, 30, 34, 37, 46, 47, 51, 56, 57, 61, 66, 67, 74, 78, 86,...
   
6. 122, 123, 130, 130, 133, 134, 136, 141, 143, 148, 151, 152,...
   
7. 153, 154, 156, 162, 164, 165, 182, 189];
   
8. CensorIPN=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,...
   
9.   1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
   
10. 
    
11. ImmPeroxPos =[...
    
12. 22, 23, 38, 42, 73, 77, 89, 115, 144];
    
13. CensorIPP=[0,0,0,0,0,0,0,0,1];
    
14. 
    
15. %number of observed (non-censored)
    
16. k1 = sum(1-CensorIPN)   %16
    
17. k2 = sum(1-CensorIPP)   %8
    
18. 
    
19. % MLE estimators of rate lambda for 2 samples.
    
20. hatlam1 = k1/sum(ImmPeroxNeg)  %0.0042
    
21. hatlam2 = k2/sum(ImmPeroxPos)  %0.0128
    
22. 
    
23. 
    
24. varlam1 = k1/(sum(ImmPeroxNeg))^2  %0.0042
    
25. varlam2 = k2/(sum(ImmPeroxPos))^2  %0.0128
    
26. 
    
27. z0975 = norminv(0.975);
    
28. [hatlam1 - z0975*sqrt(varlam1), hatlam1 + z0975*sqrt(varlam1)]
    
29. 
    
30. exp([log(hatlam1) - z0975*sqrt(1/k1) log(hatlam1) + z0975*sqrt(1/k1)])
    
31. 
    
32. 
    
33. [reclambdahat1 lamci1] = mle(ImmPeroxNeg, ...
    
34.    'distribution','exponential','censoring',CensorIPN)  
    
35.    % 237.6250
    
36.    % 153.6769  415.7289
    
37. [reclambdahat2 lamci2] = mle(ImmPeroxPos, ...
    
38.    'distribution','exponential','censoring',CensorIPP)
    
39.    % 77.8750
    
40.    % 43.1959   180.3793
    
41. %(MATLAB parametrization) scale to rate
    
42. lambdahat1 = 1/reclambdahat1  %0.0042
    
43. lambdahat2 = 1/reclambdahat2  %0.0128
    
44. 
    
45. z = (log(hatlam1) - log(hatlam2))/sqrt(1/k1 + 1/k2)  %-2.5763
    
46. p = normcdf(z)  %0.0050
    
47. 
    
48. z0975 = norminv(0.975);
    
49. [hatlam1 - z0975*sqrt(varlam1), hatlam1 + z0975*sqrt(varlam1)]
    
50.   %0.0021    0.0063
    
51.   
    
52. [hatlam1 - z0975* hatlam1/sqrt(k1), hatlam1 + z0975* hatlam1/sqrt(k1)]
    
53.   %0.0021    0.0063
    
54.   
    
55.   
    
56. exp([log(lambdahat1) - z0975*sqrt(1/k1) ...
    
57.       log(lambdahat1) + z0975*sqrt(1/k1)])  
    
58.   %0.0026    0.0069
    
59. 1./lamci1 %0.0024 0.0065
    
60.    

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1. % Muenchow flower (waiting time) data
   
2. % female is a 1x47 vector of waiting times
   
3. % cen is a 1x47 vector or censor indicators
   
4. % flowers is a 47x2 vector
   
5. % s0 = kaplan meier estimate
   
6. female=[1 2 4 4 5 6 7 7 8 8 8 9 14 15 18 18 19 23 23 26 28 29 29 29 30 32 35 35 37 39 43 56 57 59 67 71 75 75 78 81 90 94 96 96 100 102 105];
   
7. censor=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 1];
   
8. cen=1-censor;
   
9. flowers=[female',cen'];
   
10. % [ff xx vv]=KMcdfSM(female',cen',1);
    
11. %  plot(xx, ff)
    
12. kmplot([female',cen'])

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1. % Muenchow flower (waiting time) data
   
2. % female is a 1x47 vector of waiting times
   
3. % cen is a 1x47 vector or censor indicators
   
4. % flowers is a 47x2 vector
   
5. % s0 = kaplan meier estimate
   
6. female=[1 2 4 4 5 6 7 7 8 8 8 9 14 15 18 18 19 23 23 26 28 29 29 29 30 32 35 35 37 39 43 56 57 59 67 71 75 75 78 81 90 94 96 96 100 102 105];
   
7. censor=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 1];
   
8. 
   
9. flowers=[female',censor'];
   
10. %s0=KMcdfSM(female',cen',1);  
    
11. kmplot(flowers)
    
12. print -depsc 'C:\Springer\Survival\Survivaleps\muenchow.eps'
    

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1. Winsorized mean. A robust location measure that preserves sample size
   
2. is the winsorized mean. Similarly to a trimmed mean, a winsorized mean identifies
   
3. outlying observations, but instead of trimming them the observations are
   
4. replaced by either the minimum or maximum of the trimmed sample, depending
   
5. on if the trimming is done from below or above (Fig. 2.3c).
   
6. The winsorized mean is not a built-in MATLAB function. However, it can
   
7. be calculated easily by the following code:
   
8. alpha=20;
   
9. sa = sort(car);
   
10. sa(1:floor( n*alpha/200 )) = sa(floor( n*alpha/200 ) + 1);
    
11. sa(end-floor( n*alpha/200 ):end) = ...
    
12. sa(end-floor( n*alpha/200 ) - 1);
    
13. winsmean = mean(sa) % winsmean = 21.9632

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看看，谢谢楼主

Statistics for Bioengineering Sciences With Matlab and WinBUGS Support_Brani Vidakovic 2011.

take a look

thank you very much

The author integrates introductory statistics for engineers and  introductory biostatistics as a single textbook heavily oriented to computation and hands on approaches.