Akaike H. (1973). Information theory and an extension of the maximum likelihood principle 2nd Intl Symp in Information Theory.

Akaike H. (1974). A new look at the statistical model identification IEEE Trans Appl Comp. 19

Alizadeh F. (1995). Interior point methods in semidefinite programming with applications to combinatorial optimization SIAM J Optim. 5

Ben-Tal A, Nemirovski A. (2001). Lectures on modern convex optimization: Analysis, algorithms, and engineering applications.

Bennet KP, Mangasarian O. (1992). Robust linear programming discrimination of two linearly inseparable sets Optimization Methods And Software. 1

Bhattacharyya C. (2004). Second order cone programming formulations for feature selection J Mach Learn Res. 5

Boyd S, Vandenberghe L. (1996). Semidefinite programming SIAM Review. 38

Boyd S, Vandenberghe L, Wu SP. (1998). Determinant maximization with linearmatrix inequality constraints SIAM J Matrix Analysis and Appl. 19

Carmichael JP. (1976). The autoregressive method: A method for approximating and estimating positive functions Unpublished doctoral dissertation, SUNY Buffalo.

Cox DR, Lewis PAW. (1966). The statistical analysis of series of events.

Eggermont PPB, LaRiccia VN. (2001). Maximum penalized likelihood estimation Density Estimation. 1

Fletcher R. (1987). Practical methods of optimization (2nd ed).

Fushiki T, Horiuchi S, Tsuchiya T. (2003). A new computational approach to density estimation with semidefinite programming Research Memorandum 898 Tokyo Institute of Statistical Mathematics.

Good IJ, Gaskins RA. (1971). Nonparametric roughness penalties for probablity densities Biometrika. 58

Good IJ, Gaskins RA. (1980). Density estimation and bump-hunting by the penalized likelihood method exemplified by scattering and meteorite data J Am Stat Assoc. 75

Graepel T, Herbrich R. (2004). Invariant pattern recognition by semidefinite programming machines Advances in neural information processing systems. 16

Hjort NL, Glad IK. (1995). Nonparametric density estimation with a parametric start Ann Stat. 23

Jordan MI, Bartlett P, Cristianini N, Ghaoui LE, Lanckriet GRG. (2004). Learning the kernel matrix with semidefinite programming Journal Of Machine Learning Research. 5

Jordan MI, Lanckriet GRG, Bhattacharyya C, El_ghaoui L. (2002). A robust minimax approach to classification J Mach Learn Res. 3

Kim S, Waki H, Kojima M, Muramatsu M. (2005). Sums of squares and semidefinite programming relaxations for polynomial optimization problems with structured sparsity Tech Rep B-411 Tokyo Department of Mathematical and Computing Sciences.

Kojima M, Yamashita M, Fujisawa K. (2003). Implementation and evaluation of SDPA 6.0 (SemiDefinite Programming Algorithm 6.0) Optimization Methods Software. 18

Mclachlan GJ, Peel D. (2000). Finite mixture models.

Nesterov Y. (2000). Squared functional systems and optimization problems High Performance Optimization.

Nesterov Y, Nemirovskii A. (1994). Interior-point methods for convex programming.

Nocedal J, Wright SJ. (1999). Numerical optimization.

Oja E, Hyvarinen A, Karunen J. (2001). Independent component analysis.

Parzen E. (1979). Nonparametric statistical data modeling J Am Stat Assoc. 74

Roeder K. (1990). Density estimation with confidence sets exemplified by superclusters and voids in the galaxies J Am Stat Soc. 85

Sakamoto Y, Ishiguro M. (1984). A Bayesian approach to the probability density estimation Ann Inst Stat Math. 36

Scott DW. (1992). Multivariate density estimation: Theory, practice, and visualization.

Silverman BW. (1986). Density Estimation for Statistics and Data Analysis, Monographs on Statistics and Applied Probability. 26

Sturm JF. (1999). Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones Optim Methods and Software. 11

Tanabe K, Sagae M. (1999). An empirical Bayes method for nonparametric density estimation Cooperative Research Report Of The Institute Of Statistical Mathematics. 118

Tapia RA, Thompson JR. (1978). Nonparametric probability density estimation.

Todd MJ, Monteiro RDC. (2000). Path following methods Handbook of semidefinite programming; Theory, algorithms, and applications.

Todd MJ, Toh KC, Tutuncu RH. (2003). Solving semidefinite-quadratic-linear programs using SDPT3 Mathematical Programming. 95

Toh KC. (1999). Primal-dual path-following algorithms for determinant maximization problems with linear matrix inequalities Computational Optimization And Applications. 14

Vandenberghe L, Wolkowicz H, Saigal R. (2000). Handbook of semidefinite programming: Theory, algorithms, and applications.

Vapnik V. (1995). The Nature of Statistical Learning Theory.

Wand MP, Jones MC. (1995). Kernel smoothing.

Weisberg S. (1985). Applied linear regression.

Xia Y, Tsuchiya T. (2006). An extension of the standard polynomial-time primal dual path-following algorithm to the weighted determinant maximization problem with semidefinite constraints Research Memorandum 980 Tokyo Institute of Statistical Mathematics.