Abstract: An apparatus selects variables from a plurality of variables in a model that expresses a relationship between a linear predictor and an expectation value of a response variable or a probability of the response variable having certain values, by using a variable selecting model that expresses the linear predictor as a sum of a constant and a linear combination of the candidate explanatory variables and their corresponding coefficients, the apparatus including a constraint acquisition unit for acquiring a constraint that defines a set of possible values for each of the coefficients; an estimation unit for calculating an estimate of the respective coefficients and an estimate of the constant under the constraint, using plural data; and a selection unit for selecting, as the desired explanatory variable, the candidate explanatory variable corresponding to the coefficient of which the estimate is calculated to be non-zero.

Abstract: Provided is an apparatus which selects desired explanatory variables from a plurality of candidate explanatory variables in a statistical model that expresses, by a predetermined function, a relationship between a linear predictor and an expectation value of a response variable or a probability of the response variable having certain values, by using a variable selecting model that expresses the linear predictor as a sum of a constant and a linear combination of the candidate explanatory variables and their corresponding coefficients, the apparatus including a sign condition acquisition unit for acquiring sign conditions for at least one of the coefficients; an estimator for calculating an estimate of the respective coefficients and an estimate of the constant under the sign conditions, using plural data; and a selection unit for selecting, as the desired explanatory variable, the candidate explanatory variable corresponding to the coefficient of which the estimate is calculated to be non-zero.

Abstract: Provided is a program causing a computer to execute: a response probability estimation data acquiring step (S201) for acquiring response probability estimation data that defines a relationship between the value of the original variable and a response probability that shows a probability of the response variable being a certain value; an original variable data acquiring step (S202) for acquiring original variable data including realization of the original variable; and an explanatory variable value calculating step (S203, S204) for calculating as an explanatory variable value, an original variable score obtained by calculating an estimated value of the response probability from the realization of the original variable by use of the realization of the original variable and the response probability estimation data, and substituting the estimated value to inverse function of distribution function of predetermined probability distribution.

Abstract: Provided is an apparatus which selects desired explanatory variables from a plurality of candidate explanatory variables in a statistical model that expresses, by a predetermined function, a relationship between a linear predictor and an expectation value of a response variable or a probability of the response variable having certain values, by using a variable selecting model that expresses the linear predictor as a sum of a constant and a linear combination of the candidate explanatory variables and their corresponding coefficients, the apparatus including a sign condition acquisition unit for acquiring sign conditions for at least one of the coefficients; an estimator for calculating an estimate of the respective coefficients and an estimate of the constant under the sign conditions, using plural data; and a selection unit for selecting, as the desired explanatory variable, the candidate explanatory variable corresponding to the coefficient of which the estimate is calculated to be non-zero.

Abstract: A method of carrying out random number generation processing uses a GPU including a plurality of blocks each including at least one core, the random number generation processing including update processing of updating state vectors and conversion processing of converting the updated state vectors into random numbers having another distribution. The method includes carrying out, by one of the plurality of blocks, the update processing (S3), and carrying out, by the plurality of blocks, the conversion processing in parallel based on results of the update processing (S9). Therefore, it is possible to more efficiently generate a random number sequence which is the same as the one obtained through random number generation processing performed in a serial manner, by parallelizing a single random number generator in a GPU.

Abstract: A method of carrying out random number generation processing uses a GPU including a plurality of blocks each including at least one core, the random number generation processing including update processing of updating state vectors and conversion processing of converting the updated state vectors into random numbers having another distribution. The method includes carrying out, by one of the plurality of blocks, the update processing (S3), and carrying out, by the plurality of blocks, the conversion processing in parallel based on results of the update processing (S9). Therefore, it is possible to more efficiently generate a random number sequence which is the same as the one obtained through random number generation processing performed in a serial manner, by parallelizing a single random number generator in a GPU.

Abstract: The risk measures of a portfolio are calculated with speed. Concerning a loss for each of the sectors to which the companies constituting the portfolio belong, the Laplace transform of a conditional probability distribution in which factors affecting management indexes are respective values is calculated in advance and stored in a storage unit as data. The stored data is retrieved as an approximate value and used when a numerical calculation of an integral is performed, to perform the integration. Consequently, as it becomes possible to quickly calculate the Laplace transform of a density function and a distribution function, it is possible to calculate the density function and the distribution function by Laplace inversion and calculate the risk measures of the portfolio.

Abstract: The risk measures of a portfolio are calculated with speed. Concerning a loss for each of the sectors to which the companies constituting the portfolio belong, the Laplace transform of a conditional probability distribution in which factors affecting management indexes are respective values is calculated in advance and stored in a storage unit as data. The stored data is retrieved as an approximate value and used when a numerical calculation of an integral is performed, to perform the integration. Consequently, as it becomes possible to quickly calculate the Laplace transform of a density function and a distribution function, it is possible to calculate the density function and the distribution function by Laplace inversion and calculate the risk measures of the portfolio.