Why High Dimensional Modeling in Actuarial Science?
... be counter-intuitive from a non-statistical perspective. These methods also lack theoretical diagnostic tools due to their non-parametric nature. Even within each technique there are many subtle variations that are not fully reviewed by the statistical community. All of these issues contribute to re ...
... be counter-intuitive from a non-statistical perspective. These methods also lack theoretical diagnostic tools due to their non-parametric nature. Even within each technique there are many subtle variations that are not fully reviewed by the statistical community. All of these issues contribute to re ...
International Inflation and Interest Rates
... In a Bayesian analysis one fixes a prior on the unknown parameter, observes the data, and obtains the posterior distribution of the parameter given the data. For a number of problems the posterior cannot be obtained in closed form and one uses instead the Markov chain simulation method, which produc ...
... In a Bayesian analysis one fixes a prior on the unknown parameter, observes the data, and obtains the posterior distribution of the parameter given the data. For a number of problems the posterior cannot be obtained in closed form and one uses instead the Markov chain simulation method, which produc ...
Discrete-Time Methods for the Analysis of Event Histories Author(s
... change in party controlling the presidency only occurs quadrennially in the month of January. In such cases a discretetime model is clearly more appropriate than a continuous-time model. Second, in other situations events can occur at any point in time, but available data record only the particular ...
... change in party controlling the presidency only occurs quadrennially in the month of January. In such cases a discretetime model is clearly more appropriate than a continuous-time model. Second, in other situations events can occur at any point in time, but available data record only the particular ...
Word
... The input of GAMMA is a dataset D which has d observed variables Vi and a function variable C. Vi is a voxel in a MR image. C is a function variable which can either be a demographic variable such as age, or a clinical variable reflecting performance on a neuropsychological battery of tests. The out ...
... The input of GAMMA is a dataset D which has d observed variables Vi and a function variable C. Vi is a voxel in a MR image. C is a function variable which can either be a demographic variable such as age, or a clinical variable reflecting performance on a neuropsychological battery of tests. The out ...
Clustering census data: comparing the performance of
... The first and most important conclusion that can be drawn from this study is that SOM is less prone to local optima than k-means. During our tests it is quite evident that the search space is better explored by SOM. This is due to the effect of the neighborhood parameter which forces units to move a ...
... The first and most important conclusion that can be drawn from this study is that SOM is less prone to local optima than k-means. During our tests it is quite evident that the search space is better explored by SOM. This is due to the effect of the neighborhood parameter which forces units to move a ...
Iteration complexity of randomized block
... its entirety, but instead arrives incomplete in pieces and blocks over time, with each block “corresponding to” one variable, it may not be realistic (for various reasons such as “memory” and “patience” described above) to wait for the entire data set to arrive before the optimization process is sta ...
... its entirety, but instead arrives incomplete in pieces and blocks over time, with each block “corresponding to” one variable, it may not be realistic (for various reasons such as “memory” and “patience” described above) to wait for the entire data set to arrive before the optimization process is sta ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.