References
- Cattaneo, M.D., Jansson, M., and Ma, X. (2020), ‘Simple Local Polynomial Density Estimators’, Journal of the American Statistical Association, 115, 1449–1455.
- Chaudhuri, P., and Marron, J.S. (1999), ‘SiZer for Exploration of Structures in Curves’, Journal of the American Statistical Association, 94, 807–823.
- Chen, S.X. (1999), ‘Beta Kernel Estimators for Density Functions’, Computational Statistics & Data Analysis, 31, 131–145.
- Chen, S.X. (2000), ‘Probability Density Function Estimation Using Gamma Kernels’, Annals of the Institute of Statistical Mathematics, 52, 471–480.
- Gordon, L. (1994), ‘A Stochastic Approach to the Gamma Function’, American Mathematical Monthly, 101, 858–865.
- Härdle, W., Marron, J.S., and Wand, M.P. (1990), ‘Bandwidth Choice for Density Derivatives’, Journal of the Royal Statistical Society, Series B, 52, 223–232.
- Hildenbrand, K., and Hildenbrand, W. (1986), ‘On the Mean Income Effect: A Data Analysis of the U.K. Family Expenditure Survey’, in Contributions to Mathematical Economics: in Honor of Gérard Debreu, ed. W. Hildenbrand and A. Mas-Colell, Amsterdam: North-Holland, pp. 247–268.
- Hirukawa, M. (2018), Asymmetric Kernel Smoothing: Theory and Applications in Economics and Finance, Singapore: Springer.
- Jones, M.C. (2009), ‘Kumaraswamy's Distribution: A Beta-Type Distribution with Some Tractability Advantages’, Statistical Methodology, 6, 70–81.
- Kumaraswamy, P. (1980), ‘A Generalized Probability Density Function for Double-Bounded Random Processes’, Journal of Hydrology, 46, 79–88.
- Lomax, K.S. (1954), ‘Business Failures: Another Example of the Analysis of Failure Data’, Journal of the American Statistical Association, 49, 847–852.
- Müller, H.-G., and Gasser, T. (1979), ‘Optimal Convergence Properties of Kernel Estimates of Derivatives of a Density Function’, in Smoothing Techniques for Curve Estimation: Proceedings of a Workshop Held in Heidelberg, April 2–4, 1979, ed. T. Gasser and M. Rosenblatt, Berlin: Springer-Verlag, pp. 144–154.
- Stone, C.J. (1980), ‘Optimal Rates of Convergence for Nonparametric Estimators’, Annals of Statistics, 8, 1348–1360.
- Vella, F., and Verbeek, M. (1998), ‘Whose Wages Do Unions Raise? A Dynamic Model of Unionism and Wage Rate Determination for Young Men’, Journal of Applied Econometrics, 13, 163–183.
- Wand, M.P., and Jones, M.C. (1995), Kernel Smoothing, Boca Raton, FL: Chapman & Hall/CRC.
- Wasserman, L. (2006), All of Nonparametric Statistics, New York: Springer.
- Ye, Z.-S., and Chen, N. (2017), ‘Closed-Form Estimators for the Gamma Distribution Derived From Likelihood Equations’, American Statistician, 71, 177–181.