In the present work, Bi3+ was used to substitute for Nd3+ in the NdNbO4 ceramic and pure fergusonite solid solution was formed within 20 mol. % substitutions. Microwave dielectric permittivity of the ...(Nd1‐xBix)NbO4 (x ≤ 0.2) ceramics increased linearly with x value due to the larger ionic polarizability of Bi3+ than Nd3+. Excellent microwave dielectric properties with a permittivity (εr) ~22.5, a Qf (Q = quality factor, f = resonant frequency) ~50 000 GHz, and a TCF ~−9 ppm/°C were obtained in the (Nd0.9Bi0.1)NbO4 ceramic. This method might work in other fergusonite‐type rare‐earth ortho‐niobates.
Temperature coefficient of NdNbO4 microwave ceramic was adjusted to near zero by Bi substitutions.
In this paper, we analyze the length‐biased and partly interval‐censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing ...methods that focus on low‐dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high‐dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, we explore a valid inference method for handling high‐dimensional length‐biased and interval‐censored survival data with measurement error in covariates under the accelerated failure time model. We primarily employ the SIMEX method to correct for measurement error effects and propose the boosting procedure to do variable selection and estimation. The proposed method is able to handle the case that the dimension of covariates is larger than the sample size and enjoys appealing features that the distributions of the covariates are left unspecified.
In gene expression data analysis framework, ultrahigh dimensionality and measurement error are ubiquitous features. Therefore, it is crucial to correct measurement error effects and make variable ...selection when fitting a regression model. In this paper, we introduce a python package BOOME, which refers to BOOsting algorithm for Measurement Error in binary responses and ultrahigh-dimensional predictors. We primarily focus on logistic regression and probit models with responses, predictors, or both contaminated with measurement error. The BOOME aims to address measurement error effects, and employ boosting procedure to make variable selection and estimation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
In the present work, Bi
3+
was used to substitute for Nd
3+
in the NdNbO
4
ceramic and pure fergusonite solid solution was formed within 20 mol. % substitutions. Microwave dielectric ...permittivity of the (Nd
1‐
x
B
i
x
)NbO
4
(x ≤ 0.2) ceramics increased linearly with x value due to the larger ionic polarizability of Bi
3+
than Nd
3+
. Excellent microwave dielectric properties with a permittivity (
ε
r
) ~22.5, a Qf (Q = quality factor, f = resonant frequency) ~50 000
GH
z, and a
TCF
~−9 ppm/°C were obtained in the (Nd
0.9
Bi
0.1
)NbO
4
ceramic. This method might work in other fergusonite‐type rare‐earth ortho‐niobates.
Analysis of gene expression data is an attractive topic in the field of bioinformatics, and a typical application is to classify and predict individuals’ diseases or tumors by treating gene ...expression values as predictors. A primary challenge of this study comes from ultrahigh-dimensionality, which makes that (i) many predictors in the dataset might be non-informative, (ii) pairwise dependence structures possibly exist among high-dimensional predictors, yielding the network structure. While many supervised learning methods have been developed, it is expected that the prediction performance would be affected if impacts of ultrahigh-dimensionality were not carefully addressed. In this paper, we propose a new statistical learning algorithm to deal with multi-classification subject to ultrahigh-dimensional gene expressions. In the proposed algorithm, we employ the model-free feature screening method to retain informative gene expression values from ultrahigh-dimensional data, and then construct predictive models with network structures of selected gene expression accommodated. Different from existing supervised learning methods that build predictive models based on entire dataset, our approach is able to identify informative predictors and dependence structures for gene expression. Throughout analysis of a real dataset, we find that the proposed algorithm gives precise classification as well as accurate prediction, and outperforms some commonly used supervised learning methods.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In statistical analysis or supervised learning, classification has been an attractive topic. Typically, a main goal is to adopt predictors to characterize the primarily interested binary random ...variables. To model a binary response and predictors, parametric structures, such as logistic regression models or probit models, are perhaps commonly used approaches. However, due to the convenience of data collection, existence of non-informative variables as well as inevitability of measurement error in both responses and predictors become ubiquitous. The simultaneous appearance of these complex features make data analysis become challenging. To address those concerns, we propose a valid inferential method to deal with measurement error and handle variable selection simultaneously. Specifically, we focus on logistic regression or probit models, and propose estimating functions by incorporating corrected responses and predictors. After that, we develop the boosting procedure with error-eliminated estimating functions accommodated to do variable selection and estimation. To justify the proposed method, we examine the convergence of the boosting algorithm and rigorously establish the theoretical results. Through numerical studies, we find that the proposed method accurately retains informative predictors and gives precise estimators, and its performance is generally better than that without measurement error correction. The
supplementary materials
of this article, including proofs of theoretical results and computer code, are available online.
In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have ...been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High‐dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error‐contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.
Feature screening is a useful and popular tool to detect informative predictors for ultrahigh‐dimensional data before developing statistical analysis or constructing statistical models. While a large ...body of feature screening procedures has been developed, most methods are restricted to examine either continuous or discrete responses. Moreover, even though many model‐free feature screening methods have been proposed, additional assumptions are imposed in those methods to ensure their theoretical results. To address those difficulties and provide simple implementation, in this paper we extend the rank‐based coefficient of correlation to develop a feature screening procedure. We show that this new screening criterion is able to deal with continuous and binary responses. Theoretically, the sure screening property is established to justify the proposed method. Simulation studies demonstrate that the predictors with nonlinear and oscillatory trajectories are successfully retained regardless of the distribution of the response. Finally, the proposed method is implemented to analyze two microarray datasets.
