1,725,270 research outputs found
Summary statistics for inhomogeneous marked point processes
No full text© 2015, The Institute of Statistical Mathematics, Tokyo. We propose new summary statistics for intensity-reweighted moment stationary marked point processes with particular emphasis on discrete marks. The new statistics are based on the n-point correlation functions and reduce to cross J- and D-functions when stationarity holds. We explore the relationships between the various functions and discuss their explicit forms under specific model assumptions. We derive ratio-unbiased minus sampling estimators for our statistics and illustrate their use on a data set of wildfires
On New moment Estimation of Parameters of the Gamma Distribution Using Its Characterization
No full text出處：Annals of the Institute of Statistical Mathematics, Vol.54, No. 4. The Institute of Statistical Mat
Estimation of central shapes of error distributions in linear regression problems
No full textConsider a linear regression model subject to an error distribution which is symmetric about 0 and varies regularly at 0 with exponent ζ. We propose two estimators of ζ, which characterizes the central shape of the error distribution. Both methods are motivated by the well-known Hill estimator, which has been extensively studied in the related problem of estimating tail indices, but substitute reciprocals of small L p residuals for the extreme order statistics in its original definition. The first method requires careful choices of p and the number k of smallest residuals employed for calculating the estimator. The second method is based on subsampling and works under less restrictive conditions on p and k. Both estimators are shown to be consistent for ζ and asymptotically normal. A simulation study is conducted to compare our proposed procedures with alternative estimates of ζ constructed using resampling methods designed for convergence rate estimation. © 2012 The Institute of Statistical Mathematics, Tokyo.link_to_subscribed_fulltex
Explicit Estimators under m-Dependence for a Multivariate Normal Distribution
No full textThe problemof estimating parameters of amultivariate normal p-dimensional random vector is considered for a banded covariance structure reflecting mdependence. A simple non-iterative estimation procedure is suggested which gives an explicit, unbiased and consistent estimator of the mean and an explicit and consistent estimator of the covariance matrix for arbitrary p and m.Preliminary version published as Research Report 2008:3 at the Centre of Biostochastics Swedish University of Agricultural Sciences.The original publication is available at www.springerlink.com:Martin Ohlson, Zhanna Andrushchenko and Dietrich von Rosen, Explicit Estimators under m-Dependence for a Multivariate Normal Distribution, 2011, Annals of the Institute of Statistical Mathematics, (63), 1, 29-42.http://dx.doi.org/10.1007/s10463-008-0213-1Copyright: Springer Science Business Mediahttp://www.springerlink.com
A new instrumental variable estimation for diffusion processes
No full textWe consider the problem of parametric inference from continuous sample paths of the diffusion processes {x(t)} generated by the system of possibly nonstationary and/or nonlinear Ito stochastic differential equations. We propose a new instrumental variable estimator of the parameter whose pivotal statistic has a Gaussian distribution for all possible values of parameter. The new estimator enables us to construct exact level-α confidence intervals and tests for the parameter in the possibly non-stationary and/or nonlinear diffusion processes. Applications to several non-stationary and/or nonlinear diffusion processes are considered as examples. ©2005 The Institute of Statistical Mathematics
High-dimensional sign-constrained feature selection and grouping
No full text© 2020, The Institute of Statistical Mathematics, Tokyo. In this paper, we propose a non-negative feature selection/feature grouping (nnFSG) method for general sign-constrained high-dimensional regression problems that allows regression coefficients to be disjointly homogeneous, with sparsity as a special case. To solve the resulting non-convex optimization problem, we provide an algorithm that incorporates the difference of convex programming, augmented Lagrange and coordinate descent methods. Furthermore, we show that the aforementioned nnFSG method recovers the oracle estimate consistently, and that the mean-squared errors are bounded. Additionally, we examine the performance of our method using finite sample simulations and applying it to a real protein mass spectrum dataset
Nisihira Sigeki, Studies on the social surveys of the Institute of statistical Mathematics.
No full textFrisch Y. E. Nisihira Sigeki, Studies on the social surveys of the Institute of statistical Mathematics.. In: Revue française de sociologie, 1960, 1-3. pp. 361-362
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
No full textLet G be the graph corresponding to the graphical model of nearest neighbor interaction in a Gaussian character. We study Natural Exponential Families (NEF) of Wishart distributions on convex cones Q G and P G , where P G is the cone of tridiagonal positive definite real symmetric matrices, and Q G is the dual cone of P G . The Wishart NEF that we construct include Wishart distributions considered earlier for models based on decomposable(chordal) graphs. Our approach is, however, different and allows us to study the basic objects of Wishart NEF on the cones Q G and P G . We determine Riesz measures generating Wishart exponential families on Q G and P G , and we give the quadratic construction of these Riesz measures and exponential families. The mean, inverse-mean, covariance and variance functions, as well as moments of higher order, are studied and their explicit formulas are given. © 2018, The Institute of Statistical Mathematics, Tokyo.TT202
Semiparametric efficient estimators in heteroscedastic error models
No full textIn the mean regression context, this study considers several frequently encountered heteroscedastic error models where the regression mean and variance functions are specified up to certain parameters. An important point we note through a series of analyses is that different assumptions on standardized regression errors yield quite different efficiency bounds for the corresponding estimators. Consequently, all aspects of the assumptions need to be specifically taken into account in constructing their corresponding efficient estimators. This study clarifies the relation between the regression error assumptions and their, respectively, efficiency bounds under the general regression framework with heteroscedastic errors. Our simulation results support our findings; we carry out a real data analysis using the proposed methods where the Cobb–Douglas cost model is the regression mean. © 2017, The Institute of Statistical Mathematics, Tokyo
Converting information into probability measures with the Kullback-Leibler divergence
No full textThis paper uses a decision theoretic approach for updating a probability measure representing beliefs about an unknown parameter. A cumulative loss function is considered, which is the sum of two terms: one depends on the prior belief and the other one on further information obtained about the parameter. Such information is thus converted to a probability measure and the key to this process is shown to be the Kullback-Leibler divergence. The Bayesian approach can be derived as a natural special case. Some illustrations are presented. © The Institute of Statistical Mathematics, Tokyo 2012
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