Showing 1 to 10 of 1032 matching Articles
Results per page:
Export (CSV)
By
Liu, Tianqing; Yuan, Xiaohui; Sun, Jianguo
Doubly truncated data often arise when event times are observed only if they fall within subjectspecific intervals. We analyze doubly truncated data using nonparametric transformation models, where an unknown monotonically increasing transformation of the response variable is equal to an unknown monotonically increasing function of a linear combination of the covariates plus a random error with an unspecified logconcave probability density function. Furthermore, we assume that the truncation variables are conditionally independent of the response variable given the covariates and leave the conditional distributions of truncation variables given the covariates unspecified. For estimation of regression parameters, we propose a weighted rank (WR) estimation procedure and establish the consistency and asymptotic normality of the resulting estimator. The limiting covariance matrix of the WR estimator can be estimated by a resampling technique, which does not involve nonparametric density estimation or numerical derivatives. A numerical study is conducted and suggests that the proposed methodology works well in practice, and an illustration based on real data is provided.
more …
By
Abdulle, Assyr ; Garegnani, Giacomo
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomise ODE solvers by adding a random forcing term, we show that a probability measure over the numerical solution of ODEs can be obtained by introducing suitable random time steps in a classical time integrator. This intrinsic randomisation allows for the conservation of geometric properties of the underlying deterministic integrator such as mass conservation, symplecticity or conservation of first integrals. Weak and mean square convergence analysis is derived. We also analyse the convergence of the Monte Carlo estimator for the proposed random time step method and show that the measure obtained with repeated sampling converges in the mean square sense independently of the number of samples. Numerical examples including chaotic Hamiltonian systems, chemical reactions and Bayesian inferential problems illustrate the accuracy, robustness and versatility of our probabilistic numerical method.
more …
By
Kleinke, Kristian; Reinecke, Jost; Salfrán, Daniel; Spiess, Martin
Show all (4)
In this chapter missing data procedures and techniques are reviewed and discussed. Among them are both, adhoc methods but also more sophisticated techniques including maximum likelihood estimation, weighting and imputation. We discuss pros and cons of the different approaches and techniques, and give practical advice which procedure might be suited best in a given scenario because valid inferences in applied research can only be expected based on informed decisions. A conclusion of this chapter will be that there is not the one method or technique that works best under every possible scenario.
more …
By
Tamae, Hiromasa; Irie, Kaoru; Kubokawa, Tatsuya
The closedform estimators proposed by Ye and Chen (Am Stat 71(2):177–181, 2017) for the gamma distribution can be derived by the scoreadjusted method, and in the orthogonal reparameterization, the asymptotic variances are compared with the maximum likelihood and moment estimators. This method is also useful for providing closedform estimators for the beta distribution.
more …
By
Hamilton, Gregory L.; Muldrow, Melody
When President Lyndon Johnson enlisted aid from economists, social scientists, and policymakers for his War on Poverty, we still find it difficult four decades later maneuvering through the maze of conceptualizing and measuring poverty (Celllini et al. 2008). Is it time to reevaluate how we measure poverty? Would an alternative technique such as shift share analysis provide a better measure of poverty which could lead to a better understanding of poverty?
more …
By
AlNajjar, Nabil I.; Pomatto, Luciano
We study independent random variables (Z_{i})_{i∈I} aggregated by integrating with respect to a nonatomic and finitely additive probability ν over the index set I. We analyze the behavior of the resulting random average ${\int }_I Z_i d\nu (i)$. We establish that any ν that guarantees the measurability of ${\int }_I Z_i d\nu (i)$ satisfies the following law of large numbers: for any collection (Z_{i})_{i∈I} of uniformly bounded and independent random variables, almost surely the realized average ${\int }_I Z_i d\nu (i)$ equals the average expectation ${\int }_I E[Z_i]d\nu (i)$.
more …
By
Karimuzzaman, Md.; Moyazzem Hossain, Md.; Rahman, Azizur
The number of ever born children is one of the main components of population dynamics that determine the size, structure, as well as the composition of a countries’ population. Children ever born refer to the number of children born alive to the person up to a specified reference date and served as a response variable here. A secondary dataset is used in this paper that is obtained from a countrywide representative survey entitled Bangladesh Demographic and Health Survey (BDHS) 2014. This study aims to identify the socioeconomic and demographic factors influencing children ever born to the women of 15–49 years old in Bangladesh. The first attempt of this paper is to identify the bestfitted model among generalized Poisson, Negative Binomial, truncated, COM and finite mixture regression model form. The results suggest that among the model considered in this study Finite Mixture Negative Binomial Regression with three components gives the bestfitted model to estimate the number of ever born children in Bangladesh. It reveals that respondents age, residential status, family size and intention of using contraception have shown positive impact and respondents education, drinking water, toilet facility, religious status, household head age, wealth index, age at first birth, and husband education shows a negative impact on ever born children.
more …
By
Wang, Xiuli; Song, Yunquan; Zhang, Shuxia
In this paper, we study the weighted quantile average estimation technique for the parameter in additive partially linear models with missing covariates, which is proved to be an efficient method. The proposed method is based on optimally combining information over different quantiles via multiple quantile regression. We establish asymptotic normality of the weighted quantile average estimators when the selection probability is known, estimated using the nonparametrical method and parametrical method, respectively. Moreover, we compute optimal weights by minimizing asymptotic variance and then obtain the corresponding optimal weighted quantile average estimators. To examine the finite performance of our proposed method, we use the numerical simulations and apply to model time sober for the patients from a rehabilitation center. Simulation results and data analysis further verify that the proposed method is an efficient and safe alternative to both the WCQR method and WLS method.
more …
