-
1
-
-
0036645099
-
Serum Protein Fingerprinting Coupled With a Pattern-Matching Algorithm Distinguishes Prostate Cancer From Benign Prostate Hyperplasia and Healthy Men
-
Adam, B.-L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazares, L. H., Semmes, O. J., Schellhammer, P. F., Yasui, Y., Feng, Z., et al. (2002), “Serum Protein Fingerprinting Coupled With a Pattern-Matching Algorithm Distinguishes Prostate Cancer From Benign Prostate Hyperplasia and Healthy Men,” Cancer Research, 62, 3609–3614.
-
(2002)
Cancer Research
, vol.62
, pp. 3609-3614
-
-
Adam, B.-L.1
Qu, Y.2
Davis, J.W.3
Ward, M.D.4
Clements, M.A.5
Cazares, L.H.6
Semmes, O.J.7
Schellhammer, P.F.8
Yasui, Y.9
Feng, Z.10
-
2
-
-
0000501656
-
Information Theory and an Extension of the Maximum Likelihood Principle
-
eds. B. Petran, and F. Csaaki, Budapest: Akadeemiai Kiadi, pp. 267–281
-
Akaike, H., (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in International Symposium on Information Theory, eds. B. Petran, and F. Csaaki, Budapest:Akadeemiai Kiadi, pp. 267–281.
-
(1973)
International Symposium on Information Theory
-
-
Akaike, H.1
-
3
-
-
0002431823
-
A Time Series Approach to Numerical Differentiation
-
Anderssen, R., and Bloomfield, P., (1974), “A Time Series Approach to Numerical Differentiation,” Technometrics, 16, 69–75.
-
(1974)
Technometrics
, vol.16
, pp. 69-75
-
-
Anderssen, R.1
Bloomfield, P.2
-
4
-
-
84859107033
-
On Quantile Quantile Plots for Generalized Linear Models
-
Augustin, N. H., Sauleau, E.-A., and Wood, S. N., (2012), “On Quantile Quantile Plots for Generalized Linear Models,” Computational Statistics & Data Analysis, 56, 2404–2409.
-
(2012)
Computational Statistics & Data Analysis
, vol.56
, pp. 2404-2409
-
-
Augustin, N.H.1
Sauleau, E.-A.2
Wood, S.N.3
-
6
-
-
77953833620
-
-
Belitz, C., Brezger, A., Kneib, T., Lang, S., and Umlauf, N., (2015), “Bayesx:Software for Bayesian Inference in Structured Additive Regression Models,” available at http://www.statistik.lmu.de/~bayesx/bayesx.html.
-
(2015)
Bayesx: Software for Bayesian Inference in Structured Additive Regression Models
-
-
Belitz, C.1
Brezger, A.2
Kneib, T.3
Lang, S.4
Umlauf, N.5
-
7
-
-
0011241944
-
Approximate Inference in Generalized Linear Mixed Models
-
Breslow, N. E., and Clayton, D. G., (1993), “Approximate Inference in Generalized Linear Mixed Models,” Journal of the American Statistical Association, 88, 9–25.
-
(1993)
Journal of the American Statistical Association
, vol.88
, pp. 9-25
-
-
Breslow, N.E.1
Clayton, D.G.2
-
8
-
-
26444547624
-
Generalized Structured Additive Regression Based on Bayesian p-Splines
-
Brezger, A., and Lang, S., (2006), “Generalized Structured Additive Regression Based on Bayesian p-Splines,” Computational Statistics & Data Analysis, 50, 967–991.
-
(2006)
Computational Statistics & Data Analysis
, vol.50
, pp. 967-991
-
-
Brezger, A.1
Lang, S.2
-
9
-
-
0001685079
-
An Estimate for the Condition Number of a Matrix
-
Cline, A. K., Moler, C. B., Stewart, G. W., and Wilkinson, J. H., (1979), “An Estimate for the Condition Number of a Matrix,” SIAM Journal on Numerical Analysis, 16, 368–375.
-
(1979)
SIAM Journal on Numerical Analysis
, vol.16
, pp. 368-375
-
-
Cline, A.K.1
Moler, C.B.2
Stewart, G.W.3
Wilkinson, J.H.4
-
10
-
-
0000336139
-
Regression Models and Life Tables
-
Series B, 34
-
Cox, D., (1972), “Regression Models and Life Tables,” Journal of the Royal Statistical Society, Series B, 34, 187–220.
-
(1972)
Journal of the Royal Statistical Society
, pp. 187-220
-
-
Cox, D.1
-
11
-
-
0348186338
-
-
Cambridge, UK: Cambridge University Press
-
Davison, A. C., (2003), Statistical Models, Cambridge, UK:Cambridge University Press.
-
(2003)
Statistical Models
-
-
Davison, A.C.1
-
12
-
-
25444532788
-
Flexible Smoothing With B-Splines and Penalties
-
Eilers, P. H. C., and Marx, B. D., (1996), “Flexible Smoothing With B-Splines and Penalties,” Statistical Science 11, 89–121.
-
(1996)
Statistical Science
, vol.11
, pp. 89-121
-
-
Eilers, P.H.C.1
Marx, B.D.2
-
13
-
-
8644257675
-
Penalized Structured Additive Regression for Space-Time Data: A Bayesian Perspective
-
Fahrmeir, L., Kneib, T., and Lang, S., (2004), “Penalized Structured Additive Regression for Space-Time Data:A Bayesian Perspective,” Statistica Sinica, 14, 731–761.
-
(2004)
Statistica Sinica
, vol.14
, pp. 731-761
-
-
Fahrmeir, L.1
Kneib, T.2
Lang, S.3
-
14
-
-
84910663544
-
-
New York: Springer
-
Fahrmeir, L., Kneib, T., Lang, S., and Marx, B., (2013), “Regression Models, New York:Springer.
-
(2013)
Regression Models
-
-
Fahrmeir, L.1
Kneib, T.2
Lang, S.3
Marx, B.4
-
15
-
-
0035649015
-
Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random Field Priors
-
Fahrmeir, L., and Lang, S., (2001), “Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random Field Priors,” Applied Statistics, 50, 201–220.
-
(2001)
Applied Statistics
, vol.50
, pp. 201-220
-
-
Fahrmeir, L.1
Lang, S.2
-
16
-
-
78651278795
-
On the Behaviour of Marginal and Conditional AIC in Linear Mixed Models
-
Greven, S., and Kneib, T., (2010), “On the Behaviour of Marginal and Conditional AIC in Linear Mixed Models,” Biometrika, 97, 773–789.
-
(2010)
Biometrika
, vol.97
, pp. 773-789
-
-
Greven, S.1
Kneib, T.2
-
17
-
-
84972488102
-
Generalized Additive Models” (with discussion)
-
Hastie, T., and Tibshirani, R., (1986), “Generalized Additive Models” (with discussion), Statistical Science, 1, 297–318.
-
(1986)
Statistical Science
, vol.1
, pp. 297-318
-
-
Hastie, T.1
Tibshirani, R.2
-
19
-
-
80052029170
-
Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models)
-
Kass, R. E., and Steffey, D., (1989), “Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models),” Journal of the American Statistical Association, 84, 717–726.
-
(1989)
Journal of the American Statistical Association
, vol.84
, pp. 717-726
-
-
Kass, R.E.1
Steffey, D.2
-
20
-
-
84934804898
-
Bayesian Structured Additive Distributional Regression for Multivariate Responses
-
Series C, 64
-
Klein, N., Kneib, T., Klasen, S., and Lang, S., (2014), “Bayesian Structured Additive Distributional Regression for Multivariate Responses,” Journal of the Royal Statistical Society, Series C, 64, 569–591.
-
(2014)
Journal of the Royal Statistical Society
, pp. 569-591
-
-
Klein, N.1
Kneib, T.2
Klasen, S.3
Lang, S.4
-
21
-
-
84938510555
-
Bayesian Structured Additive Distributional Regression With an Application to Regional Income Inequality in Germany
-
Klein, N., Kneib, T., Lang, S., and Sohn, A., (2015), “Bayesian Structured Additive Distributional Regression With an Application to Regional Income Inequality in Germany,” Annals of Applied Statistics, 9, 1024–1052.
-
(2015)
Annals of Applied Statistics
, vol.9
, pp. 1024-1052
-
-
Klein, N.1
Kneib, T.2
Lang, S.3
Sohn, A.4
-
22
-
-
0020333131
-
Random-Effects Models for Longitudinal Data
-
Laird, N. M., and Ware, J. H., (1982), “Random-Effects Models for Longitudinal Data,” Biometrics, 38, 963–974.
-
(1982)
Biometrics
, vol.38
, pp. 963-974
-
-
Laird, N.M.1
Ware, J.H.2
-
23
-
-
50949086329
-
A Note on Conditional AIC for Linear Mixed-Effects Models
-
Liang, H., Wu, H., and Zou, G., (2008), “A Note on Conditional AIC for Linear Mixed-Effects Models,” Biometrika, 95, 773–778.
-
(2008)
Biometrika
, vol.95
, pp. 773-778
-
-
Liang, H.1
Wu, H.2
Zou, G.3
-
24
-
-
79953654016
-
Practical Variable Selection for Generalized Additive Models
-
Marra, G., and Wood, S. N., (2011), “Practical Variable Selection for Generalized Additive Models,” Computational Statistics & Data Analysis, 55, 2372–2387.
-
(2011)
Computational Statistics & Data Analysis
, vol.55
, pp. 2372-2387
-
-
Marra, G.1
Wood, S.N.2
-
25
-
-
84857058351
-
Coverage Properties of Confidence Intervals for Generalized Additive Model Components
-
Marra, G., and Wood, S. N., (2012), “Coverage Properties of Confidence Intervals for Generalized Additive Model Components,” Scandinavian Journal of Statistics, 39, 53–74.
-
(2012)
Scandinavian Journal of Statistics
, vol.39
, pp. 53-74
-
-
Marra, G.1
Wood, S.N.2
-
26
-
-
0032493547
-
Direct Generalized Additive Modeling With Penalized Likelihood
-
Marx, B. D., and Eilers, P. H., (1998), “Direct Generalized Additive Modeling With Penalized Likelihood,” Computational Statistics and Data Analysis, 28, 193–209.
-
(1998)
Computational Statistics and Data Analysis
, vol.28
, pp. 193-209
-
-
Marx, B.D.1
Eilers, P.H.2
-
28
-
-
33845606053
-
Bayesian Confidence Intervals for Smoothing Splines
-
Nychka, D., (1988), “Bayesian Confidence Intervals for Smoothing Splines,” Journal of the American Statistical Association, 83, 1134–1143.
-
(1988)
Journal of the American Statistical Association
, vol.83
, pp. 1134-1143
-
-
Nychka, D.1
-
29
-
-
62849093921
-
Smoothing Parameter Selection for a Class of Semiparametric Linear Models
-
Series B, 71
-
Reiss, P. T., and Ogden, T. R., (2009), “Smoothing Parameter Selection for a Class of Semiparametric Linear Models,” Journal of the Royal Statistical Society, Series B, 71, 505–523.
-
(2009)
Journal of the Royal Statistical Society
, pp. 505-523
-
-
Reiss, P.T.1
Ogden, T.R.2
-
30
-
-
18544382833
-
Generalized Additive Models for Location, Scale and Shape
-
Series C, 54
-
Rigby, R., and Stasinopoulos, D. M., (2005), “Generalized Additive Models for Location, Scale and Shape,” Journal of the Royal Statistical Society, Series C, 54, 507–554.
-
(2005)
Journal of the Royal Statistical Society
, pp. 507-554
-
-
Rigby, R.1
Stasinopoulos, D.M.2
-
31
-
-
84906062174
-
Automatic Smoothing Parameter Selection in GAMLSS With an Application to Centile Estimation
-
Rigby, R. A., and Stasinopoulos, D. M., (2014), “Automatic Smoothing Parameter Selection in GAMLSS With an Application to Centile Estimation,” Statistical Methods in Medical Research, 23, 318–332.
-
(2014)
Statistical Methods in Medical Research, 23, 318–332
-
-
Rigby, R.A.1
Stasinopoulos, D.M.2
-
32
-
-
62849120031
-
Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations
-
Series B, 71
-
Rue, H., Martino, S., and Chopin, N., (2009), “Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations,” Journal of the Royal Statistical Society, Series B, 71, 319–392.
-
(2009)
Journal of the Royal Statistical Society
, pp. 319-392
-
-
Rue, H.1
Martino, S.2
Chopin, N.3
-
33
-
-
0012891890
-
-
New York: Cambridge University Press
-
Ruppert, D., Wand, M. P., and Carroll, R. J., (2003), Semiparametric Regression, New York:Cambridge University Press.
-
(2003)
Semiparametric Regression
-
-
Ruppert, D.1
Wand, M.P.2
Carroll, R.J.3
-
34
-
-
84897892561
-
A Unifying Approach to the Estimation of the Conditional Akaike Information in Generalized Linear Mixed Models
-
Säfken, B., Kneib, T., van Waveren, C.-S., and Greven, S., (2014), “A Unifying Approach to the Estimation of the Conditional Akaike Information in Generalized Linear Mixed Models,” Electronic Journal of Statistics, 8, 201–225.
-
(2014)
Electronic Journal of Statistics
, vol.8
, pp. 201-225
-
-
Säfken, B.1
Kneib, T.2
van Waveren, C.-S.3
Greven, S.4
-
35
-
-
0001303166
-
Laplace Approximation of High Dimensional Integrals
-
Series B, 57
-
Shun, Z., and McCullagh, P., (1995), “Laplace Approximation of High Dimensional Integrals,” Journal of the Royal Statistical Society, Series B, 57, 749–760.
-
(1995)
Journal of the Royal Statistical Society
, pp. 749-760
-
-
Shun, Z.1
McCullagh, P.2
-
36
-
-
0001995852
-
Some Aspects of the Spline Smoothing Approach to Non-Parametric Regression Curve Fitting
-
Series B, 47
-
Silverman, B. W., (1985), “Some Aspects of the Spline Smoothing Approach to Non-Parametric Regression Curve Fitting,” Journal of the Royal Statistical Society, Series B, 47, 1–53.
-
(1985)
Journal of the Royal Statistical Society
, pp. 1-53
-
-
Silverman, B.W.1
-
37
-
-
84922538014
-
Structured Additive Regression Models: An r Interface to Bayesx
-
Umlauf, N., Adler, D., Kneib, T., Lang, S., and Zeileis, A., (2015), “Structured Additive Regression Models:An r Interface to Bayesx,” Journal of Statistical Software, 63, 1–46.
-
(2015)
Journal of Statistical Software
, vol.63
, pp. 1-46
-
-
Umlauf, N.1
Adler, D.2
Kneib, T.3
Lang, S.4
Zeileis, A.5
-
38
-
-
0000939344
-
Bayesian Confidence Intervals for the Cross Validated Smoothing Spline
-
Series B, 45
-
Wahba, G., (1983), “Bayesian Confidence Intervals for the Cross Validated Smoothing Spline,” Journal of the Royal Statistical Society, Series B, 45, 133–150.
-
(1983)
Journal of the Royal Statistical Society
, pp. 133-150
-
-
Wahba, G.1
-
39
-
-
0000608177
-
A Comparison of GCV and GML for Choosing the Smoothing Parameter in the Generalized Spline Smoothing Problem
-
Wahba, G., (1985), “A Comparison of GCV and GML for Choosing the Smoothing Parameter in the Generalized Spline Smoothing Problem,” The Annals of Statistics, pp. 1378–1402.
-
(1985)
The Annals of Statistics
, pp. 1378-1402
-
-
Wahba, G.1
-
40
-
-
0034364945
-
Modelling and Smoothing Parameter Estimation With Multiple Quadratic Penalties
-
Series B, 62
-
Wood, S. N., (2000), “Modelling and Smoothing Parameter Estimation With Multiple Quadratic Penalties,” Journal of the Royal Statistical Society, Series B, 62, 413–428.
-
(2000)
Journal of the Royal Statistical Society
, pp. 413-428
-
-
Wood, S.N.1
-
41
-
-
33645690420
-
Low-Rank Scale-Invariant Tensor Product Smooths for Generalized Additive Mixed Models
-
Wood, S. N., (2006), “Low-Rank Scale-Invariant Tensor Product Smooths for Generalized Additive Mixed Models,” Biometrics, 62, 1025–1036.
-
(2006)
Biometrics
, vol.62
, pp. 1025-1036
-
-
Wood, S.N.1
-
42
-
-
78650862532
-
Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
-
Series B, 73
-
Wood, S. N., (2011), “Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models,” Journal of the Royal Statistical Society, Series B, 73, 3–36.
-
(2011)
Journal of the Royal Statistical Society
, pp. 3-36
-
-
Wood, S.N.1
-
43
-
-
0001460390
-
Vector Generalized Additive Models
-
Series B
-
Yee, T. W., and Wild, C., (1996), “Vector Generalized Additive Models,” Journal of the Royal Statistical Society, Series B, 481–493.
-
(1996)
Journal of the Royal Statistical Society
, pp. 481-493
-
-
Yee, T.W.1
Wild, C.2
-
44
-
-
80455176896
-
Conditional Akaike Information Criterion for Generalized Linear Mixed Models
-
Yu, D., and Yau, K. K., (2012), “Conditional Akaike Information Criterion for Generalized Linear Mixed Models,” Computational Statistics & Data Analysis, 56, 629–644.
-
(2012)
Computational Statistics & Data Analysis
, vol.56
, pp. 629-644
-
-
Yu, D.1
Yau, K.K.2
|