-
1
-
-
0001878035
-
Multiple regression analysis
-
Ralston A, Wilf HS, (eds), Wiley, New York
-
Efroymson MA. Multiple regression analysis. In: Ralston A, Wilf HS, editors. Mathematical methods for digital computers. New York: Wiley; 1960
-
(1960)
Mathematical methods for digital computers
-
-
Efroymson, M.A.1
-
2
-
-
0007267360
-
Why won’t stepwise methods die?
-
Thompson B. Why won’t stepwise methods die? Meas Eval Couns Dev. 1989;21(4):146–8
-
(1989)
Meas Eval Couns Dev
, vol.21
, Issue.4
, pp. 146-148
-
-
Thompson, B.1
-
3
-
-
0001159321
-
The impact of model selection on inference in linear regression
-
Hurvich CM, Tsai CL. The impact of model selection on inference in linear regression. Am Stat. 1990;44(3):214–7
-
(1990)
Am Stat
, vol.44
, Issue.3
, pp. 214-217
-
-
Hurvich, C.M.1
Tsai, C.L.2
-
6
-
-
2442695245
-
What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models
-
Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66:411–21
-
(2004)
Psychosom Med
, vol.66
, pp. 411-421
-
-
Babyak, M.A.1
-
7
-
-
33746801903
-
Why do we still use stepwise modelling in ecology and behaviour?
-
Whittingham MJ, Stephens PA, Bradbury RB, Freckleton RP. Why do we still use stepwise modelling in ecology and behaviour? J Anim Ecol. 2006;75(5):1182–9
-
(2006)
J Anim Ecol
, vol.75
, Issue.5
, pp. 1182-1189
-
-
Whittingham, M.J.1
Stephens, P.A.2
Bradbury, R.B.3
Freckleton, R.P.4
-
8
-
-
85183788740
-
-
Department of Economics, University of Oxford, Oxford
-
Castle JL, Fawcett NWP, Hendry DF. Evaluating automatic model selection, Technical Report 474. Oxford: Department of Economics, University of Oxford; 2010
-
(2010)
Evaluating automatic model selection, Technical Report 474
-
-
Castle, J.L.1
Fawcett, N.W.P.2
Hendry, D.F.3
-
9
-
-
84888059665
-
Stopping stepwise: Why stepwise and similar selection methods are bad, and what you should use
-
Flom PL, Cassell DL. Stopping stepwise: why stepwise and similar selection methods are bad, and what you should use. In: NESUG 2007 proceedings. 2007
-
(2007)
NESUG 2007 Proceedings
-
-
Flom, P.L.1
Cassell, D.L.2
-
10
-
-
84973777204
-
Stepwise regression and stepwise discriminant analysis need not apply here: a guidelines editorial
-
Thompson B. Stepwise regression and stepwise discriminant analysis need not apply here: a guidelines editorial. Educ Psychol Meas. 1995;55:525–34
-
(1995)
Educ Psychol Meas
, vol.55
, pp. 525-534
-
-
Thompson, B.1
-
12
-
-
0002441724
-
Problems with stepwise methods—better alternatives
-
Thompson B, (ed), JAI Press, Greenwich
-
Huberty CJ. Problems with stepwise methods—better alternatives. In: Thompson B, editor. Advances in social science methodology, vol. 1. Greenwich: JAI Press; 1989
-
(1989)
Advances in social science methodology
, vol.1
-
-
Huberty, C.J.1
-
14
-
-
73449083353
-
Variable selection: current practice in epidemiological studies
-
Walter S, Tiemeier H. Variable selection: current practice in epidemiological studies. Eur J Epidemiol. 2009;24(12):733–6
-
(2009)
Eur J Epidemiol
, vol.24
, Issue.12
, pp. 733-736
-
-
Walter, S.1
Tiemeier, H.2
-
15
-
-
77957149553
-
A survey of variable selection methods in two Chinese epidemiology journals
-
Liao H, Lynn HS. A survey of variable selection methods in two Chinese epidemiology journals. BMC Med Res Methodol. 2010;10:87. 10.1186/1471-2288-10-87
-
(2010)
BMC Med Res Methodol
, vol.10
, pp. 87
-
-
Liao, H.1
Lynn, H.S.2
-
16
-
-
61849177980
-
-
Wiley, New York
-
Rachev ST, Mittnik S, Fabozzi FJ, Focardi SM, Jašić T. Financial econometrics: from basics to advanced modeling techniques. New York: Wiley; 2006
-
(2006)
Financial econometrics: from basics to advanced modeling techniques
-
-
Rachev, S.T.1
Mittnik, S.2
Fabozzi, F.J.3
Focardi, S.M.4
Jašić, T.5
-
21
-
-
0002283033
-
From data mining to knowledge discovery in databases
-
Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Mag. 1996;17(3):37–54
-
(1996)
AI Mag
, vol.17
, Issue.3
, pp. 37-54
-
-
Fayyad, U.1
Piatetsky-Shapiro, G.2
Smyth, P.3
-
22
-
-
85053282767
-
Foreword
-
Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA, (eds), Springer, New York
-
Kecman V. Foreword. In: Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA, editors. Data mining: a knowledge discovery approach. New York: Springer; 2007
-
(2007)
Data mining: a knowledge discovery approach
-
-
Kecman, V.1
-
24
-
-
0002958315
-
Knowledge discovery in real databases: a report on the IJCAI-89 workshop
-
Piatetsky-Shapiro G. Knowledge discovery in real databases: a report on the IJCAI-89 workshop. AI Mag. 1991;11(5):68–70
-
(1991)
AI Mag
, vol.11
, Issue.5
, pp. 68-70
-
-
Piatetsky-Shapiro, G.1
-
27
-
-
85015581756
-
A comment on Daniel Klein’s “A plea to economists who favor liberty
-
Tullock G. A comment on Daniel Klein’s “A plea to economists who favor liberty”. East Econ J. 2001;27(2):203–7
-
(2001)
East Econ J
, vol.27
, Issue.2
, pp. 203-207
-
-
Tullock, G.1
-
31
-
-
84903161818
-
Big data: new tricks for econometrics
-
Varian HR. Big data: new tricks for econometrics. J Econ Perspect. 2014;28(2):3–27
-
(2014)
J Econ Perspect
, vol.28
, Issue.2
, pp. 3-27
-
-
Varian, H.R.1
-
33
-
-
84960126500
-
The deluge of spurious correlations in big data
-
Calude CS, Longo G. The deluge of spurious correlations in big data. Found Sci. 2016. 10.1007/s10699-016-9489-4
-
(2016)
Found Sci
-
-
Calude, C.S.1
Longo, G.2
-
34
-
-
0033213971
-
Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis
-
Steyerberg EW, Eijkemans MJC, Habbema JDF. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J Clin Epidemiol. 1999;52(10):935–42
-
(1999)
J Clin Epidemiol
, vol.52
, Issue.10
, pp. 935-942
-
-
Steyerberg, E.W.1
Eijkemans, M.J.C.2
Habbema, J.D.F.3
-
35
-
-
85004844353
-
Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables
-
Derksen S, Keselman HJ. Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables. Br J Math Stat Psychol. 1992;45(2):265–82
-
(1992)
Br J Math Stat Psychol
, vol.45
, Issue.2
, pp. 265-282
-
-
Derksen, S.1
Keselman, H.J.2
-
36
-
-
84947389475
-
The development of numerical credit evaluation systems
-
Mayers JH, Forgy EW. The development of numerical credit evaluation systems. J Am Stat Assoc. 1963;58(303):799–806
-
(1963)
J Am Stat Assoc
, vol.58
, Issue.303
, pp. 799-806
-
-
Mayers, J.H.1
Forgy, E.W.2
-
37
-
-
0002156597
-
Multiple regression analysis and mass assessment: a review of the issues
-
Mark J, Goldberg MA. Multiple regression analysis and mass assessment: a review of the issues. Apprais J. 2001;56:89–109
-
(2001)
Apprais J
, vol.56
, pp. 89-109
-
-
Mark, J.1
Goldberg, M.A.2
-
38
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
Guyan I, Weston J, Barnhill S, Vopnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422
-
(2002)
Mach Learn
, vol.46
, pp. 389-422
-
-
Guyan, I.1
Weston, J.2
Barnhill, S.3
Vopnik, V.4
-
39
-
-
85053272491
-
RSSI-based supervised learning for uncooperative direction-finding
-
Altun Y, (ed), Lecture Notes Computer, Springer, Cham
-
Mukherjee T, Duckat M, Kumar P, Paquet JD, Rodriguez D, Haulcomb M, George K, Pasiliao E. RSSI-based supervised learning for uncooperative direction-finding. In: Altun Y, editor. Machine learning and knowledge discovery in databases. ECML PKDD 2017, vol. 10536., Lecture Notes in ComputerCham: Springer; 2015
-
(2015)
Machine learning and knowledge discovery in databases. ECML PKDD 2017
, vol.10536
-
-
Mukherjee, T.1
Duckat, M.2
Kumar, P.3
Paquet, J.D.4
Rodriguez, D.5
Haulcomb, M.6
George, K.7
Pasiliao, E.8
-
44
-
-
84947678943
-
A critique of some ridge regression methods
-
Smith G, Campbell F. A critique of some ridge regression methods. J Am Stat Assoc. 1980;75(369):74–81
-
(1980)
J Am Stat Assoc
, vol.75
, Issue.369
, pp. 74-81
-
-
Smith, G.1
Campbell, F.2
|