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Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
 
 
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Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) [Hardcover]

T.J. Hastie , R.J. Tibshirani , D.R. Cox , N. Reid , Valerie Isham , Thomas A. Louis , Howell Tong , Niels Keiding

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Inside This Book (Learn More)
First Sentence
One of the most popular and useful tools in data analysis is the linear regression model. Read the first page
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1988  1989  ace  additive  age  algorithm  analysis  approach  approximation  backfitting  basis  between  case  chapter  components  convergence  cubic  curves  data  degrees  described  df  different  distribution  effect  eigenvalues  equations  error  estimate  estimating  estimation  example  exercise  fig  figure  first  fit  fitted  fitting  form  freedom  functions  generalized  given  ii  kernel  knots  let  level  likelihood  linear  matrix  may  mean  method  model  neighbourhood  new  nonparametric  note  number  observations  parameter  plot  points  predictor  problem  procedure  regression  represent  residuals  response  results  section  set  show  simple  since  smooth  smoother  smoothing  solution  space  splines  statist  step  surface  terms  time  transformations  two  use  used  values  variable  variance  vector  weighted  weights  x2 
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