![[BACK]](/cvsweb/icons/back.gif){kind=icon}
Return to chap5.terms CVS log ![[TXT]](/cvsweb/icons/text.gif){kind=icon}
![[DIR]](/cvsweb/icons/dir.gif){kind=icon}
Up to [Research Unix] / researchv10dc / vol2 / index|
Return to chap5.terms CVS log | Up to [Research Unix] / researchv10dc / vol2 / index |
1.1 root 1: Gaussian additive model
2: Gram-Schmidt method
3: Hilbert space
4: Local-likelihood estimation
5: Los Angeles air-pollution data
6: Newton-Raphson algorithm
7: Newton-Raphson iterations
8: Newton-Raphson update
9: ace criterion
10: additive predictor
11: additive proportional hazards
12: additive spline model
13: adjusted dependent variable regression
14: asymptotic variance-stabilizing transformation
15: backfitting algorithm
16: backfitting solutions
17: balanced additive
18: bandwidth selection
19: basis function
20: bin smoother
21: binary data
22: bootstrap approximation
23: boundary knots
24: canonical link
25: cardinal-splines
26: conditional expectation
27: conditional expectation operators
28: constant term
29: continuous response variable
30: cubic smoothing splines
31: cubic smoothing-spline operator matrix
32: cubic splines
33: cubic-spline basis functions
34: delta algorithms
35: dummy variable
36: equivalent kernel
37: equivalent kernels
38: estimating equations
39: evaluated-splines
40: exact concurvity
41: expected log-likelihood
42: exponential family models
43: fitted function
44: fitted values
45: formula language
46: generalized additive models
47: generalized cross-validated deviance
48: generalized residual
49: hazard function
50: hierarchical model
51: isotonic regression
52: jackknifed fit
53: linear effect
54: linear predictor
55: linear scatterplot smoother
56: linear system
57: local-scoring algorithm
58: log-linear models
59: logistic additive model
60: logistic regression
61: main effects
62: missing data
63: modelling interactions
64: moving average
65: multi-predictor smoothers
66: multinomial likelihood
67: multiple regression
68: natural cubic spline
69: natural parameter
70: nearest-neighbour smoother
71: non-DOS computers
72: nonhierarchical model
73: null hypothesis
74: observation weights
75: orthogonal polynomials
76: ozone concentration data
77: parametric fitting
78: partial residuals
79: partial-residual plots
80: penalized least squares
81: penalized log-likelihood
82: piecewise cubic polynomials
83: piecewise polynomials
84: pointwise standard-error bands
85: pointwise standard-error curves
86: posterior covariance
87: predictive ability
88: prior covariance
89: projection-type smoothers
90: pseudo additive models
91: reproducing-kernel Hilbert-spaces
92: resistant additive
93: ridge regression
94: scale estimate
95: scale parameter
96: score equations
97: seasonal operators
98: shrinking smoothers
99: slicing regression
100: smoothing parameter
101: smoothing parameter selection
102: smoothing spline
103: span selection
104: specialized local-scoring algorithm
105: standard-error bands
106: step size optimization
107: stepwise-additive methods
108: surface smoother
109: symmetric smoother matrices
110: tensor product bases
111: tidwell method
112: time series
113: trend component
114: unweighted running-lines smoother
115: weight function
116: weighted additive-fit operator
117: weighted cubic smoothing spline
118: weighted least-squares fit
119: weighted smoothers
120: Cox model
121: Fourier coefficients
122: S functions
123: adaptive techniques
124: additive predictor
125: adjusted dependent variable
126: alternating algorithm
127: binary data
128: comparing models
129: cross-validated deviance
130: diagonal elements
131: fitted value
132: global confidence band
133: influential points
134: linear filter
135: local-scoring algorithm
136: locally-weighted running-lines
137: logistic regression
138: matched sets
139: maximum likelihood
140: monotone transformations
141: natural splines
142: nearest neighbours
143: optimal rate
144: ozone data
145: partial residual
146: penalized least-squares criterion
147: pointwise standard-error bands
148: posterior covariance
149: posterior mean
150: proportional-odds model
151: resistant algorithm
152: running median
153: scatterplot smoother
154: scatterplot smoothing
155: seasonal effect
156: semi-parametric model
157: smoother matrix
158: smoothing parameter selection
159: smoothing-spline matrix
160: standard-error bands
161: survival data
162: symmetric nearest neighbourhood
163: target value
164: trend smoother
165: weight matrix
166: weighted additive model
167: asymptotic bias
168: bootstrap sample
169: cubic smoothing spline
170: equivalent kernel
171: estimating equations
172: expected log-likelihood
173: information matrix
174: kernel smoother
175: least-squares line
176: local-likelihood estimation
177: matched case-control data
178: modified backfitting algorithm
179: multiple linear regression
180: nonlinear smoothers
181: partial likelihood
182: posterior distribution
183: running-lines smoother
184: seasonal smoother
185: stl procedure
186: surface smoothers
187: time series
188: Kullback-Leibler distance
189: additive model
190: backfitting algorithm
191: concurvity space
192: equivalent kernel
193: local-scoring algorithm
194: locally-weighted running-lines
195: seasonal component
196: time series
197: transfer function
198: Bayesian model
199: conditional likelihood
200: cubic smoothing spline
201: generalized additive model
202: model selection
203: orthogonal projection
204: smoother matrix
205: smoothing parameter
206: unique solution
207: backfitting converges
208: interior knots
209: kernel smoothers
210: maximal correlation
211: canonical correlation
212: running mean
213: smoothing splines
214: adjusted dependent variable
215: exponential family
216: generalized linear model
217: proportional-hazards model
218: response transformation
219: optimal transformations
220: link function
221: starting functions
222: backfitting algorithm
223: ace algorithm
224: smoothing parameter
225: backfitting algorithm
226: estimating equations
227: {\em ACE and Correspondence analysis}
228: {\em ACE and canonical correlation}
229: {\em Atmospheric ozone concentration}
230: {\em Automatic backfitting}
231: {\em Average Derivative Estimation}
232: {\em CART} software
233: {\em Computation of the \GCV\ statistic}
234: {\em Delta method}
235: {\em Diabetes data}
236: {\em Efficient kernel smoothing ^{Silverman (1982)},
237: {\em Generalized cross-validation (GCV)}
238: {\em Hanning}
239: {\em Kriging}
240: {\em Kyphosis data}
241: {\em M-estimate} approaches
242: {\em M-estimation for regression}
243: {\em M-estimation}
244: {\em Mallow's $C_p$}
245: {\em Semi-parametric regression ^^{Green, P.J.}^^{Jennison,
246: {\em Slicing regression}
247: {\em TURBO} paper
248: {\em Twicing}
249: {\em Universal Kriging}
250: {\em Updating formula for running-line smooth}
251: {\em Warm cardioplegia data}
252: {\em additive model}
253: {\em additive predictor}
254: {\em additive} predictor
255: {\em adjusted dependent variable regression}
256: {\em asymptotic variance stabilizing transformation}
257: {\em backfitting algorithm}
258: {\em backfitting}
259: {\em calendar} effects
260: {\em canonical correlation}
261: {\em canonical link}
262: {\em centered} smoother
263: {\em collinearity}
264: {\em complimentary log-log}
265: {\em concurvity space}
266: {\em concurvity}
267: {\em convolution}
268: {\em curse of dimensionality}
269: {\em degrees of freedom}
270: {\em delta algorithm}
271: {\em deviance}
272: {\em digital filter})
273: {\em effective number of parameters}
274: {\em effective} dimension
275: {\em equivalent degrees of freedom}
276: {\em equivalent kernel}
277: {\em estimating} equations
278: {\em expected} log-likelihood
279: {\em frequency response functions}
280: {\em fundamental tradeoff between bias and variance}
281: {\em generalized additive models}
282: {\em generalized additive model}
283: {\em generalized linear models}
284: {\em hat} matrix
285: {\em hierarchical}
286: {\em impulse response function}
287: {\em interaction}
288: {\em leverage points}
289: {\em linear predictor}
290: {\em link function}
291: {\em local scoring}
292: {\em local-likelihood} estimation
293: {\em local-scoring procedure}
294: {\em loess}
295: {\em loess})
296: {\em logit}
297: {\em low pass}
298: {\em matched sets}
299: {\em maximal correlation}
300: {\em missing at random}
301: {\em nonparametric} nature
302: {\em odds-ratio}
303: {\em optimal transformations for correlation}
304: {\em optimal transformations for regression}
305: {\em optimal transformations}
306: {\em partial likelihood}
307: {\em powering up}
308: {\em probit}
309: {\em profile log-likelihood}
310: {\em pseudo additive models}
311: {\em pseudo smoothers}
312: {\em regression smoothers}
313: {\em representers of evaluation}
314: {\em resubstitution prediction error}
315: {\em ridge regression}
316: {\em scatterplot smoother}
317: {\em seasonal} smoother
318: {\em semi-parametric} model
319: {\em shrinking}
320: {\em shrinking} smoothers
321: {\em simple Kriging}
322: {\em smoother matrix}
323: {\em smoothing parameter}
324: {\em splitting}
325: {\em state-space} approach
326: {\em supersmoother}
327: {\em tensor product}
328: {\em thin-plate spline}
329: {\em ties}
330: {\em trading day}
331: {\em transfer function}
332: {\em transformation}
333: {\em trend} smoother
334: {\em tri-cube} weight
335: {\em twicing}
336: {\em{BOX-TIDWELL}}}
337: {\em{BRUTO}}}
338: {\em{STEP-ADDITIVE}}}
339: {\em{TURBO}}}
This archive runs on limited infrastructure. Preserving old code on modern bandwidth. Automated agents are requested to crawl responsibly.