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Scandinavian Journal of Statistics, Theory and Applications

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https://www.readbyqxmd.com/read/29033489/exact-and-approximate-statistical-inference-for-nonlinear-regression-and-the-estimating-equation-approach
#1
Eugene Demidenko
The exact density distribution of the nonlinear least squares estimator in the one-parameter regression model is derived in closed form and expressed through the cumulative distribution function of the standard normal variable. Several proposals to generalize this result are discussed. The exact density is extended to the estimating equation (EE) approach and the nonlinear regression with an arbitrary number of linear parameters and one intrinsically nonlinear parameter. For a very special nonlinear regression model, the derived density coincides with the distribution of the ratio of two normally distributed random variables previously obtained by Fieller (1932), unlike other approximations previously suggested by other authors...
September 2017: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/28439147/a-semi-parametric-transformation-frailty-model-for-semi-competing-risks-survival-data
#2
Fei Jiang, Sebastien Haneuse
In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1...
March 2017: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/28316363/analysis-of-double-single-index-models
#3
Kun Chen, Yanyuan Ma
Motivated from problems in canonical correlation analysis, reduced rank regression and sufficient dimension reduction, we introduce a double dimension reduction model where a single index of the multivariate response is linked to the multivariate covariate through a single index of these covariates, hence the name double single index model. Since nonlinear association between two sets of multivariate variables can be arbitrarily complex and even intractable in general, we aim at seeking a principal one-dimensional association structure where a response index is fully characterized by a single predictor index...
March 2017: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27867251/linear-increments-with-non-monotone-missing-data-and-measurement-error
#4
Shaun R Seaman, Daniel Farewell, Ian R White
Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non-monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non-monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both...
December 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27867250/approaches-to-the-estimation-of-the-local-average-treatment-effect-in-a-regression-discontinuity-design
#5
Aidan G O'Keeffe, Gianluca Baio
Regression discontinuity designs (RD designs) are used as a method for causal inference from observational data, where the decision to apply an intervention is made according to a 'decision rule' that is linked to some continuous variable. Such designs are being increasingly developed in medicine. The local average treatment effect (LATE) has been established as an estimator of the intervention effect in an RD design, particularly where a design's 'decision rule' is not adhered to strictly. Estimating the variance of the LATE is not necessarily straightforward...
December 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27795610/functional-mixed-effects-model-for-small-area-estimation
#6
Tapabrata Maiti, Samiran Sinha, Ping-Shou Zhong
Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area level data, and fit a varying coefficient linear mixed effect model where the varying coefficients are semi-parametrically modeled via B-splines...
September 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27647948/a-semiparametrically-efficient-estimator-of-the-time-varying-effects-for-survival-data-with-time-dependent-treatment
#7
Huazhen Lin, Zhe Fei, Yi Li
The timing of a time-dependent treatment-e.g., when to perform a kidney transplantation-is an important factor for evaluating treatment efficacy. A naïve comparison between the treated and untreated groups, while ignoring the timing of treatment, typically yields biased results that might favor the treated group because only patients who survive long enough will get treated. On the other hand, studying the effect of a time-dependent treatment is often complex, as it involves modeling treatment history and accounting for the possible time-varying nature of the treatment effect...
September 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27453626/locally-efficient-semiparametric-estimators-for-proportional-hazards-models-with-measurement-error
#8
Yuhang Xu, Yehua Li, Xiao Song
We propose a new class of semiparametric estimators for proportional hazards models in the presence of measurement error in the covariates, where the baseline hazard function, the hazard function for the censoring time, and the distribution of the true covariates are considered as unknown infinite dimensional parameters. We estimate the model components by solving estimating equations based on the semiparametric efficient scores under a sequence of restricted models where the logarithm of the hazard functions are approximated by reduced rank regression splines...
June 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27239090/fitting-cox-models-with-doubly-censored-data-using-spline-based-sieve-marginal-likelihood
#9
Zhiguo Li, Kouros Owzar
In some applications, the failure time of interest is the time from an originating event to a failure event, while both event times are interval censored. We propose fitting Cox proportional hazards models to this type of data using a spline-based sieve maximum marginal likelihood, where the time to the originating event is integrated out in the empirical likelihood function of the failure time of interest. This greatly reduces the complexity of the objective function compared with the fully semiparametric likelihood...
June 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27034534/a-proportional-hazards-regression-model-for-the-sub-distribution-with-covariates-adjusted-censoring-weight-for-competing-risks-data
#10
Peng He, Frank Eriksson, Thomas H Scheike, Mei-Jie Zhang
With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate-dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate-dependent censoring...
March 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/26985125/a-rejection-principle-for-sequential-tests-of-multiple-hypotheses-controlling-familywise-error-rates
#11
Jay Bartroff, Jinlin Song
We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together we call these conditions a "rejection principle for sequential tests," which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing...
March 1, 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/26236075/a-proof-of-bell-s-inequality-in-quantum-mechanics-using-causal-interactions
#12
James M Robins, Tyler J VanderWeele, Richard D Gill
We give a simple proof of Bell's inequality in quantum mechanics using theory from causal interaction, which, in conjunction with experiments, demonstrates that the local hidden variables assumption is false. The proof sheds light on relationships between the notion of causal interaction and interference between treatments.
June 1, 2015: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/26019387/estimating-mean-survival-time-when-is-it-possible
#13
Ying Ding, Bin Nan
For right censored survival data, it is well known that the mean survival time can be consistently estimated when the support of the censoring time contains the support of the survival time. In practice, however, this condition can be easily violated because the follow-up of a study is usually within a finite window. In this article we show that the mean survival time is still estimable from a linear model when the support of some covariate(s) with nonzero coefficient(s) is unbounded regardless of the length of follow-up...
June 1, 2015: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/26924877/marginal-and-conditional-distribution-estimation-from-double-sampled-semi-competing-risks-data
#14
Menggang Yu, Constantin T Yiannoutsos
Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach program to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete...
March 1, 2015: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/26392675/instrument-assisted-regression-for-errors-in-variables-models-with-binary-response
#15
Kun Xu, Yanyuan Ma, Liqun Wang
We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established, and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example...
March 2015: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27642219/selection-of-latent-variables-for-multiple-mixed-outcome-models
#16
Ling Zhou, Huazhen Lin, Xinyuan Song, Y I Li
Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters...
December 2014: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27030787/computing-critical-values-of-exact-tests-by-incorporating-monte-carlo-simulations-combined-with-statistical-tables
#17
Albert Vexler, Young Min Kim, Jihnhee Yu, Nicole A Lazar, Aland Hutson
Various exact tests for statistical inference are available for powerful and accurate decision rules provided that corresponding critical values are tabulated or evaluated via Monte Carlo methods. This article introduces a novel hybrid method for computing p-values of exact tests by combining Monte Carlo simulations and statistical tables generated a priori. To use the data from Monte Carlo generations and tabulated critical values jointly, we employ kernel density estimation within Bayesian-type procedures...
December 2014: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/25395718/a-predictive-study-of-dirichlet-process-mixture-models-for-curve-fitting
#18
Sara Wade, Stephen G Walker, Sonia Petrone
This paper examines the use of Dirichlet process (DP) mixtures for curve fitting. An important modelling aspect in this setting is the choice between constant or covariate-dependent weights. By examining the problem of curve fitting from a predictive perspective, we show the advantages of using covariate-dependent weights. These advantages are a result of the incorporation of covariate proximity in the latent partition. However, closer examination of the partition yields further complications, which arise from the vast number of total partitions...
September 2014: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/24904194/bayesian-transformation-models-for-multivariate-survival-data
#19
Mário DE Castro, Ming-Hui Chen, Joseph G Ibrahim, John P Klein
In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology...
March 2014: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/24578589/integrative-analysis-of-cancer-diagnosis-studies-with-composite-penalization
#20
Jin Liu, Jian Huang, Shuangge Ma
In cancer diagnosis studies, high-throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the "large d, small n" feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single datasets can be unsatisfactory. A cost-effective remedy is to conduct integrative analysis of multiple heterogeneous datasets...
March 1, 2014: Scandinavian Journal of Statistics, Theory and Applications
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