You searched for:
Journal
Computational statistics & data analysis
Remove constraint Journal: Computational statistics & data analysis
Publication Year
2019
Remove constraint Publication Year: 2019
Source
2019 v.137
Remove constraint Source: 2019 v.137

# PubAg

## Main content area

## Limit your search

- 2019[remove]18

## Search

### 18 Search Results

**1**-

**18**of

**18**

## Search Results

- Author:
- Bischofberger, Stephan M.; Hiabu, Munir; Mammen, Enno; Nielsen, Jens Perch
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 133-154
- ISSN:
- 0167-9473
- Subject:
- demography; epidemiology; insurance; mathematical theory; models; sociology
- Abstract:
- ... In-sample forecasting is a recent continuous modification of well-known forecasting methods based on aggregated data. These aggregated methods are known as age-cohort methods in demography, economics, epidemiology and sociology and as chain ladder in non-life insurance. Data is organized in a two-way table with age and cohort as indices, but without measures of exposure. It has recently been estab ...
- DOI:
- 10.1016/j.csda.2019.02.009
- https://dx.doi.org/10.1016/j.csda.2019.02.009

### 2. A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis

- Author:
- He, Shengmei; Ma, Shuangge; Xu, Wangli
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 155-169
- ISSN:
- 0167-9473
- Subject:
- discriminant analysis; models; screening
- Abstract:
- ... Cui et al. (2015) proposed a mean–variance feature-screening method based on the index MV(X|Y). By modifying MV(X|Y) with a weight function, a new index AD(X,Y) is introduced to measure the dependence between X and Y, and a corresponding feature-screening procedure called Anderson–Darling sure independence screening (AD-SIS) is proposed for ultrahigh-dimensional discriminant analysis. The sure scr ...
- DOI:
- 10.1016/j.csda.2019.02.003
- https://dx.doi.org/10.1016/j.csda.2019.02.003

- Author:
- Grömping, Ulrike; Fontana, Roberto
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 101-114
- ISSN:
- 0167-9473
- Subject:
- algorithms; computer software; statistical models
- Abstract:
- ... An algorithm for the creation of mixed level arrays with generalized minimum aberration (GMA) is proposed. GMA mixed level arrays are particularly useful for experiments involving qualitative factors: for these, the number of factor levels is often a consequence of subject matter requirements, while a priori assumptions on a statistical model are not made, apart from assuming lower order effects t ...
- DOI:
- 10.1016/j.csda.2019.01.020
- https://dx.doi.org/10.1016/j.csda.2019.01.020

- Author:
- Bui, Anh Tuan; Apley, Daniel W.
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 33-50
- ISSN:
- 0167-9473
- Subject:
- computer software; fabrics; learning; manufacturing; quality control
- Abstract:
- ... Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochast ...
- DOI:
- 10.1016/j.csda.2019.01.019
- https://dx.doi.org/10.1016/j.csda.2019.01.019

- Author:
- Castillo-Páez, Sergio; Fernández-Casal, Rubén; García-Soidán, Pilar
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 1-15
- ISSN:
- 0167-9473
- Subject:
- geostatistics; models; spatial data; variance; variance covariance matrix
- Abstract:
- ... The aim is to provide a nonparametric bootstrap method for spatial data, which can be either stationary or depart from the stationarity condition due to the presence of a non-constant trend. The proposed technique has been designed to reproduce the variability of the underlying process in an appropriate way, since it takes into account the bias effect due to the use of residuals. In certain cases, ...
- DOI:
- 10.1016/j.csda.2019.01.017
- https://dx.doi.org/10.1016/j.csda.2019.01.017

- Author:
- Mariñas-Collado, Irene; Bowman, Adrian; Macaulay, Vincent
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 285-298
- ISSN:
- 0167-9473
- Subject:
- ancestry; nationalities and ethnic groups; normal distribution; phylogeny
- Abstract:
- ... Statistical methods which enable shape information on organisms to be used to construct a phylogenetic tree and to learn how shape evolves are developed. In particular, this allows the evolution of facial curves to be used in studying relationships between and within different ethnic groups and their ancestors. The main challenge is to exploit the details of surface shape, while maintaining comput ...
- DOI:
- 10.1016/j.csda.2019.03.002
- https://dx.doi.org/10.1016/j.csda.2019.03.002

- Author:
- Liu, Baisen; Wang, Liangliang; Nie, Yunlong; Cao, Jiguo
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 233-246
- ISSN:
- 0167-9473
- Subject:
- Bayesian theory; differential equation; models; normal distribution; pharmacokinetics
- Abstract:
- ... A mixed-effects ordinary differential equation (ODE) model is proposed to describe complex dynamical systems. In order to make the inference of ODE parameters robust against the outlying observations and subjects, a class of heavy-tailed distributions is applied to model the random effects of ODE parameters and measurement errors in the data. The heavy-tailed distributions are so flexible that the ...
- DOI:
- 10.1016/j.csda.2019.03.001
- https://dx.doi.org/10.1016/j.csda.2019.03.001

- Author:
- Müller, Dominik; Czado, Claudia
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 211-232
- ISSN:
- 0167-9473
- Subject:
- data collection; models
- Abstract:
- ... To model high dimensional data, Gaussian methods are widely used since they remain tractable and yield parsimonious models by imposing strong assumptions on the data. Vine copulas are more flexible by combining arbitrary marginal distributions and (conditional) bivariate copulas. Yet, this adaptability is accompanied by sharply increasing computational effort as the dimension increases. The propos ...
- DOI:
- 10.1016/j.csda.2019.02.007
- https://dx.doi.org/10.1016/j.csda.2019.02.007

- Author:
- Matsuda, Takeru; Komaki, Fumiyasu
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 195-210
- ISSN:
- 0167-9473
- Subject:
- algorithms; models; shrinkage; variance
- Abstract:
- ... We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris. Since the EB algorithm is derived as the Expectation–Maximization algorithm applied to a simple model, it does not require heuristic parameter tuning other than tolerance. Also, it can account for the hetero ...
- DOI:
- 10.1016/j.csda.2019.02.006
- https://dx.doi.org/10.1016/j.csda.2019.02.006

- Author:
- Liu, Jicai; Xu, Peirong; Lian, Heng
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 271-284
- ISSN:
- 0167-9473
- Subject:
- Monte Carlo method; data collection; empirical research; models
- Abstract:
- ... In this paper, we focus on the estimation of the index coefficients in single-index models and develop a new procedure based on martingale difference divergence. Since the proposed procedure can capture automatically the conditional mean dependence of the response variable on the covariates, it does not involve smoothing techniques or require the commonly used assumptions in the literature of sing ...
- DOI:
- 10.1016/j.csda.2019.03.008
- https://dx.doi.org/10.1016/j.csda.2019.03.008

- Author:
- Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 67-91
- ISSN:
- 0167-9473
- Subject:
- Bayesian theory; algorithms; case studies; models; probability
- Abstract:
- ... A novel approach for parameter estimation in Bayesian networks is presented. The main idea is to introduce a hyper-prior in the Multinomial–Dirichletmodel, traditionally used for conditional distribution estimation in Bayesian networks. The resulting hierarchical model jointly estimates different conditional distributions belonging to the same conditional probability table, thus borrowing statisti ...
- DOI:
- 10.1016/j.csda.2019.02.004
- https://dx.doi.org/10.1016/j.csda.2019.02.004

- Author:
- Griffin, Maryclare; Hoff, Peter D.
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 181-194
- ISSN:
- 0167-9473
- Subject:
- analysis of variance; data collection; magnetic resonance imaging; microarray technology; plant diseases and disorders; wheat
- Abstract:
- ... Consider the problem of estimating the entries of an unknown mean matrix or tensor given a single noisy realization. In the matrix case, this problem can be addressed by decomposing the mean matrix into a component that is additive in the rows and columns, i.e. the additive ANOVA decomposition of the mean matrix, plus a matrix of elementwise effects, and assuming that the elementwise effects may b ...
- DOI:
- 10.1016/j.csda.2019.02.005
- https://dx.doi.org/10.1016/j.csda.2019.02.005

- Author:
- Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 115-132
- ISSN:
- 0167-9473
- Subject:
- Monte Carlo method; algebra; computer software; soil treatment; soil water; spatial data; variance; variance covariance matrix
- Abstract:
- ... The unknown parameters (variance, smoothness, and covariance length) of a spatial covariance function can be estimated by maximizing the joint Gaussian log-likelihood function. To overcome cubic complexity in the linear algebra, the discretized covariance function is approximated in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and O(knlogn) storage, ...
- DOI:
- 10.1016/j.csda.2019.02.002
- https://dx.doi.org/10.1016/j.csda.2019.02.002

### 14. Maximum penalized likelihood estimation of additive hazards models with partly interval censoring

- Author:
- Li, Jinqing; Ma, Jun
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 170-180
- ISSN:
- 0167-9473
- Subject:
- algorithms; models; regression analysis; system optimization
- Abstract:
- ... Existing likelihood methods for the additive hazards model with interval censored survival data are limited and often ignore the non-negative constraints on hazards. This paper proposes a maximum penalized likelihood method to fit additive hazards models with interval censoring. Our method firstly models the baseline hazard using a finite number of non-negative basis functions, and then regression ...
- DOI:
- 10.1016/j.csda.2019.02.010
- https://dx.doi.org/10.1016/j.csda.2019.02.010

- Author:
- Fuentes-García, Ruth; Mena, Ramsés H.; Walker, Stephen G.
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 92-100
- ISSN:
- 0167-9473
- Subject:
- Bayesian theory; Markov chain; Monte Carlo method; algorithms; data collection; models
- Abstract:
- ... Motivated by the Hopfield’s network, a conditional maximization routine is used in order to compute the posterior mode of a random allocation model. The proposed approach applies to a general framework covering parametric and nonparametric Bayesian mixture models, product partition models, and change point models, among others. The resulting algorithm is simple to code and very fast, thus providin ...
- DOI:
- 10.1016/j.csda.2019.02.008
- https://dx.doi.org/10.1016/j.csda.2019.02.008

- Author:
- Thaden, Hauke; Klein, Nadja; Kneib, Thomas
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 51-66
- ISSN:
- 0167-9473
- Subject:
- Bayesian theory; applied research; equations; malnutrition; models; spatial data; species richness; Africa; Asia
- Abstract:
- ... Modeling complex relationships and interactions between variables is an ongoing statistical challenge. In particular, the joint modeling of multiple response variables is of great interest in methodological and applied research. Within this context the incorporation of semiparametric predictors into Bayesian recursive simultaneous equation models is considered. Extending the existing framework by ...
- DOI:
- 10.1016/j.csda.2018.12.004
- https://dx.doi.org/10.1016/j.csda.2018.12.004

- Author:
- Ippel, L.; Kaptein, M.C.; Vermunt, J.K.
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 16-32
- ISSN:
- 0167-9473
- Subject:
- data collection; global positioning systems; mobile telephones; prediction; probability; shrinkage
- Abstract:
- ... It has become increasingly easy to collect data from individuals over long periods of time. Examples include smart-phone applications used to track movements with GPS, web-log data tracking individuals’ browsing behavior, and longitudinal (cohort) studies where many individuals are monitored over an extensive period of time. All these datasets cover a large number of individuals and collect data o ...
- DOI:
- 10.1016/j.csda.2019.01.010
- https://dx.doi.org/10.1016/j.csda.2019.01.010

- Author:
- Bergé, Laurent R.; Bouveyron, Charles; Corneli, Marco; Latouche, Pierre
- Source:
- Computational statistics & data analysis 2019 v.137 pp. 247-270
- ISSN:
- 0167-9473
- Subject:
- algorithms; data collection; models
- Abstract:
- ... Textual interaction data involving two disjoint sets of individuals/objects are considered. An example of such data is given by the reviews on web platforms (e.g. Amazon, TripAdvisor, etc.) where buyers comment on products/services they bought. A new generative model, the latent topic block model (LTBM), is developed along with an inference algorithm to simultaneously partition the elements of eac ...
- DOI:
- 10.1016/j.csda.2019.03.005
- https://dx.doi.org/10.1016/j.csda.2019.03.005