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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 148)
Statistics in Medicine     Hybrid Journal   (Followers: 134)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 50)
Sociological Methods & Research     Hybrid Journal   (Followers: 43)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 19)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Optimization Methods and Software     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Engineering With Computers     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Stochastic Models     Hybrid Journal   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

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Statistical Papers
Journal Prestige (SJR): 1.004
Citation Impact (citeScore): 1
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1613-9798 - ISSN (Online) 0932-5026
Published by Springer-Verlag Homepage  [2469 journals]
  • Correction to: Testing convexity of the generalised hazard function

    • Free pre-print version: Loading...

      Abstract: A Correction to this paper has been published: 10.1007/s00362-021-01273-w
      PubDate: 2022-08-01
       
  • Tests for heteroskedasticity in transformation models

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      Abstract: Abstract We consider a model whereby a given response variable Y following a transformation \({{\mathcal {Y}}}:=\mathcal {T}(Y)\) , satisfies some classical regression equation. In this transformation model the form of the transformation is specified analytically but incorporates an unknown transformation parameter. We develop testing procedures for the null hypothesis of homoskedasticity for versions of this model where the regression function is considered either known or unknown. The test statistics are formulated on the basis of Fourier-type conditional contrasts of a variance computed under the null hypothesis against the same quantity computed under alternatives. The limit null distribution of the test statistic is studied, as well as the behaviour of the test criterion under alternatives. Since the limit null distribution is complicated, a bootstrap version is suggested in order to actually carry out the test procedures. Monte Carlo results are included that illustrate the finite-sample properties of the new method. The applicability of the new tests on real data is also illustrated.
      PubDate: 2022-08-01
       
  • Portmanteau tests for generalized integer-valued autoregressive time
           series models

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      Abstract: Abstract In recent years, integer-valued time series attract the attention of researchers and find their applications in data analysis. Among various models, the integer-valued autoregressive (INAR) ones are of great popularity and are widely applied in practice. This paper develops some portmanteau test statistics to check the adequacy of the fitted model in a wide group of INAR processes, called generalized INAR. For this purpose, the asymptotic distributions of the test statistics are obtained and, using Monte Carlo simulation studies, their finite sample properties are derived. Besides, the results are applied in analyzing a real data example
      PubDate: 2022-08-01
       
  • Confidence intervals with higher accuracy for short and long-memory linear
           processes

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      Abstract: Abstract In this paper an easy to implement method of stochastically weighing short and long-memory linear processes is introduced. The method renders asymptotically exact size confidence intervals for the population mean which are significantly more accurate than their classic counterparts for each fixed sample size n. It is illustrated both theoretically and numerically that the randomization framework of this paper produces randomized (asymptotic) pivotal quantities, for the mean, which admit central limit theorems with smaller magnitudes of error as compared to those of their leading classic counterparts. An Edgeworth expansion result for randomly weighted linear processes whose innovations do not necessarily satisfy the Cramer condition, is established. Numerical illustrations and applications to real world data are also included.
      PubDate: 2022-08-01
       
  • Truncating the exponential with a uniform distribution

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      Abstract: Abstract For a sample of Exponentially distributed durations we aim at point estimation and a confidence interval for its parameter. A duration is only observed if it has ended within a certain time interval, determined by a Uniform distribution. Hence, the data is a truncated empirical process that we can approximate by a Poisson process when only a small portion of the sample is observed, as is the case for our applications. We derive the likelihood from standard arguments for point processes, acknowledging the size of the latent sample as the second parameter, and derive the maximum likelihood estimator for both. Consistency and asymptotic normality of the estimator for the Exponential parameter are derived from standard results on M-estimation. We compare the design with a simple random sample assumption for the observed durations. Theoretically, the derivative of the log-likelihood is less steep in the truncation-design for small parameter values, indicating a larger computational effort for root finding and a larger standard error. In applications from the social and economic sciences and in simulations, we indeed, find a moderately increased standard error when acknowledging truncation.
      PubDate: 2022-08-01
       
  • Quantile correlation coefficient: a new tail dependence measure

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      Abstract: Abstract A quantile correlation coefficient is newly defined as the geometric mean of two quantile regression slopes—that of X on Y and that of Y on X—in the same way that the Pearson correlation coefficient is related to regression coefficients. The quantile correlation is a measure of overall sensitivity of a conditional quantile of a random variable to changes in the other variable. The proposed quantile correlation can be compared across different tails within a given distribution to provide meaningful interpretations, for example, that there is stronger dependence in the left tail than overall. It can also be compared with the Pearson correlation. Neither of these two comparability within a given distribution is enabled by the existing tail-dependence correlation measures. Moreover a test for differences in the quantile correlations at different tails is proposed. The asymptotic normality of the estimated quantile correlation and the null distribution of the proposed test are established and are well supported by a Monte-Carlo study. The proposed quantile correlation methods are illustrated well by an analysis of stock return price data sets, yielding a clear indication of stronger left-tail dependence than overall dependence and stronger overall dependence than right-tail dependence.
      PubDate: 2022-08-01
       
  • Testing high-dimensional mean vector with applications

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      Abstract: Abstract A centered \(L^2\) -norm based test statistic is used for testing if a high-dimensional mean vector equals zero where the data dimension may be much larger than the sample size. Inspired by the fact that under some regularity conditions the asymptotic null distributions of the proposed test are the same as the limiting distributions of a chi-square-mixture, a three-cumulant matched chi-square-approximation is suggested to approximate this null distribution. The asymptotic power of the proposed test under a local alternative is established and the effect of data non-normality is discussed. A simulation study under various settings demonstrates that in terms of size control, the proposed test performs significantly better than some existing competitors. Several real data examples are presented to illustrate the wide applicability of the proposed test to a variety of high-dimensional data analysis problems, including the one-sample problem, paired two-sample problem, and MANOVA for correlated samples or independent samples.
      PubDate: 2022-08-01
       
  • Properties of individual differences scaling and its interpretation

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      Abstract: Abstract Indscal models consider symmetric matrices \(\varvec{B}_{k}=\varvec{X}\varvec{W}_{k}\varvec{X}'\) for \(k = 1, \ldots , K\) , where \(\varvec{X}: n \times R\) is a compromise matrix termed the group-average and \(\varvec{W}_{k}\) is a diagonal matrix of weights given by the kth individual to the R, specified in advance, columns of \(\varvec{X}\) ; non-negative weights are preferred and usually \(R < n\) . We propose a new two-phase alternating least squares (ALS) algorithm, which emphasizes the two main components (group average and weighting parameters) of the Indscal model and specifically helps with the interpretation of the model. Furthermore, it has thrown new light on the properties of the converged solution, that would be satisfied by any algorithm that minimizes the basic Indscal criterion: \(min\sum _{k=1}^{K}\Vert \varvec{B}_{k}-\varvec{X}\varvec{W}_{k}\varvec{X}'\Vert ^{2}\) where the minimization is over \(\varvec{X}\) and the \(\varvec{W}_{k}\) . The new algorithm has also proved to be a useful tool in unravelling the algebraic understanding of the role played by parameter constraints and their interpretation in variants of the Indscal model. The proposed analysis focusses on Indscal but the approach may be of more widespread interest, especially in the field of multidimensional data analysis. A major issue is that simultaneous least-squares estimates of the parameters may be found without imposing constraints. However, group average and individual weighting parameters may not be estimated uniquely, without imposing some subjective constraint that could encourage misleading interpretations. We encourage the use of linear constraints \(\sum _{k=1}^{K}\varvec{1'W}_{k}= \varvec{1'}\) , as it enables a comparison of the weights obtained (i) within group k and (ii) between the same item drawn from two or more groups. However, it is easy to exchange one system of constraints to another in a post- or pre-analysis. The new two-phase ALS algorithm (i) computes for fixed \(\varvec{X}: n \times R\) the weights \(\varvec{W}_{k}\) subject to \(\sum _{k=1}^{K}\varvec{1'W}_{k}= \varvec{1'}\) , and then (ii) keeping \(\varvec{W}_{k}\) fixed, it updates \(\varvec{X}\) . At convergence, the estimates of \(\varvec{X}: n \times R\) and the \(\varvec{W}_{k}\) will apply to all algorithms that minimize the Indscal criterion. Furthermore, we show that only at convergence an analysis-of-variance property holds on the demarcation region between over- and under-fitting. When the analysis-of-variance is valid, its validity extends over the whole matrix domain, over trace operations, and to individual matrix elements. The optimization process is unusual in that optima and local optima occur on the edges of what seem to be closely related to Heywood cases in Factor analysis.
      PubDate: 2022-08-01
       
  • Testing convexity of the generalised hazard function

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      Abstract: Abstract Let F, G be a pair of absolutely continuous cumulative distributions, where F is the distribution of interest and G is assumed to be known. The composition \(G^{-1}\circ F\) , which is referred to as the generalised hazard function of F with respect to G, provides a flexible framework for statistical inference of F under shape restrictions, determined by G, which enables the generalisation of some well-known models, such as the increasing hazard rate family. This paper is concerned with the problem of testing the null hypothesis \({\mathscr {H}}_0\) : “ \(G^{-1}\circ F\) is convex”. The test statistic is based on the distance between the empirical distribution function and a corresponding isotonic estimator, which is denoted as the greatest relatively-convex minorant of the empirical distribution with respect to G. Under \({\mathscr {H}}_0\) , this estimator converges uniformly to F, giving rise to a rather simple and general procedure for deriving families of consistent tests, without any support restriction. As an application, a goodness-of-fit test for the increasing hazard rate family is provided.
      PubDate: 2022-08-01
       
  • Real-time detection of a change-point in a linear expectile model

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      Abstract: Abstract In the present paper we address the real-time detection problem of a change-point in the coefficients of a linear model with the possibility that the model errors are asymmetrical and that the explanatory variables number is large. We build test statistics based on the cumulative sum (CUSUM) of the expectile function derivatives calculated on the residuals obtained by the expectile and adaptive LASSO expectile estimation methods. The asymptotic distribution of these statistics are obtained under the hypothesis that the model does not change. Moreover, we prove that they diverge when the model changes at an unknown observation. The asymptotic study of the test statistics under these two hypotheses allows us to find the asymptotic critical region and the stopping time, that is the observation where the model will change. The empirical performance is investigated by a comparative simulation study with other statistics of CUSUM type. Two examples on real data are also presented to demonstrate its interest in practice.
      PubDate: 2022-08-01
       
  • Asymptotic analysis of reliability measures for an imperfect dichotomous
           test

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      Abstract: Abstract To access the reliability of a new dichotomous test and to capture the random variability of its results in the absence of a gold standard, two measures, the inconsistent acceptance probability (IAP) and inconsistent rejection probability (IRP), were introduced in the literature. In this paper, we first analyze the limiting behavior of both measures as the number of test repetitions increases and derive the corresponding accuracy estimates and rates of convergence. To overcome possible limitations of IRP and IAP, we then introduce a one-parameter family of refined reliability measures, \(\Delta (k, s)\) . Such measures characterize the consistency of the results of a dichotomous test in the absence of a gold standard as the threshold for a positive aggregate test result varies. Similar to IRP and IAP, we also derive corresponding accuracy estimates and rates of convergence for \(\Delta (k, s)\) as the number k of test repetitions increases.
      PubDate: 2022-08-01
       
  • Hypothesis testing in sparse weighted stochastic block model

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      Abstract: Abstract Community detection is a fundamental task in network data mining. Various algorithms have been proposed to detect the communities of a network. However, the output of these algorithms are meaningful only if community structure exists in the network. It is necessary to statistically test the presence of community structure before applying any community detection algorithms. The existing algorithms or testing procedures mainly focus on unweighted graph, that is, the edge presence or absence is coded as a binary variable. However, most real-world networks have weights. Recently, several algorithms have been devised to detect communities in weighted networks. In this paper, we consider the fundamental problem whether community structure exists in a weighted network. Specifically, we propose a test statistic based on the number of weighted triangles and edges, derive its limiting distribution under the null hypothesis and analyze its power. The simulation results and real data application show that the proposed test can achieve high power.
      PubDate: 2022-08-01
       
  • Optimal subsampling for composite quantile regression model in massive
           data

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      Abstract: Abstract The composite quantile regression (CQR) estimator is a robust and efficient alternative to the ordinary least squares estimator and single quantile regression estimator in linear models. For massive data, two different optimal subsampling probabilities through minimizing the trace of the asymptotic variance–covariance matrix for a linearly transformed parameter estimator and the asymptotic mean squared error of the resultant estimator are proposed to downsize the data volume and reduce the computational burden. Furthermore, to improve the efficiency of the ordinary CQR, the optimal subsampling for the weighted CQR estimator is also studied. We also propose iterative subsampling procedures based on two optimal subsampling probabilities to estimate the variance–covariance matrix. The finite-sample performance of the proposed estimators is studied through simulations and an application to household electric power consumption data is also presented.
      PubDate: 2022-08-01
       
  • A generalized information criterion for high-dimensional PCA rank
           selection

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      Abstract: Abstract Principal component analysis (PCA) is a commonly used statistical tool for dimension reduction. An important issue in PCA is to determine the rank, which is the number of dominant eigenvalues of the covariance matrix. Among information-based criteria, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are the two most common ones. Both use the number of free parameters for assessing model complexity, which requires the validity of the simple spiked covariance model. As a result, AIC and BIC may suffer from the problem of model misspecification when the tail eigenvalues do not follow the simple spiked model assumption. To alleviate this difficulty, we adopt the idea of the generalized information criterion (GIC) to propose a model complexity measure for PCA rank selection. The proposed model complexity takes into account the sizes of eigenvalues and, hence, is more robust to model misspecification. Asymptotic properties of our GIC are established under the high-dimensional setting, where \(n\rightarrow \infty \) and \(p/n\rightarrow c >0\) . Our asymptotic results show that GIC is better than AIC in excluding noise eigenvalues, and is more sensitive than BIC in detecting signal eigenvalues. Numerical studies and a real data example are presented.
      PubDate: 2022-08-01
       
  • Simple powerful robust tests based on sign depth

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      Abstract: Abstract Up to now, powerful outlier robust tests for linear models are based on M-estimators and are quite complicated. On the other hand, the simple robust classical sign test usually provides very bad power for certain alternatives. We present a generalization of the sign test which is similarly easy to comprehend but much more powerful. It is based on K-sign depth, shortly denoted by K-depth. These so-called K-depth tests are motivated by simplicial regression depth, but are not restricted to regression problems. They can be applied as soon as the true model leads to independent residuals with median equal to zero. Moreover, general hypotheses on the unknown parameter vector can be tested. While the 2-depth test, i.e. the K-depth test for \(K = 2\) , is equivalent to the classical sign test, K-depth test with \(K\ge 3\) turn out to be much more powerful in many applications. A drawback of the K-depth test is its fairly high computational effort when implemented naively. However, we show how this inherent computational complexity can be reduced. In order to see why K-depth tests with \(K\ge 3\) are more powerful than the classical sign test, we discuss the asymptotic behavior of its test statistic for residual vectors with only few sign changes, which is in particular the case for some alternatives the classical sign test cannot reject. In contrast, we also consider residual vectors with alternating signs, representing models that fit the data very well. Finally, we demonstrate the good power of the K-depth tests for some examples including high-dimensional multiple regression.
      PubDate: 2022-07-30
       
  • Accelerated failure time vs Cox proportional hazards mixture cure models:
           David vs Goliath'

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      Abstract: Abstract A mixture cure model relies on a model for the cure probability and a model for the survival function of the uncured subjects. For the latter, one often uses a Cox proportional hazards model. We show the identifiability of this model under weak assumptions. The model assumes that the cure threshold is the same for all values of the covariates, which might be unrealistic in certain situations. An alternative mixture cure model is the accelerated failure time (AFT) model. We also show the identifiability of this model under minimal assumptions. The cure threshold in this model depends on the covariates, which often leads to a better fit of the data. This is especially true when the follow-up period is insufficient for certain values of the covariates. We study these two models via simulations both when the follow-up is sufficient and when it is insufficient. Moreover, the two models are applied to data coming from a breast cancer clinical trial. We show that the AFT and the Cox model both fit the data well in the region of sufficient follow-up, but differ drastically outside that region.
      PubDate: 2022-07-29
       
  • Conditional characteristic feature screening for massive imbalanced data

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      Abstract: Abstract Using conditional characteristic function as a screening index, a new model-free screening procedure is proposed to deal with variable screening problems in large-scale high-dimensional imbalanced data analysis. For binary response, our results show that the screening index under full data is proportional to the screening index under case–control sampling, an important sampling property for imbalanced data. This conclusion implies that we can apply this screening method to imbalanced data. Surely, the most appealing feature of the screening index is that it can be expressed as a simple linear combination of two first-order moments, so it is computationally simple. In addition, we successfully extend this method to multiple response. The theoretical properties are established under regularity conditions. To compare the performance of our method with its competitors, extensive simulations are conducted, which shows that the proposed procedure performs well in both the linear and nonlinear models. Finally, a real data analysis is investigated to further illustrate the effectiveness of the new method.
      PubDate: 2022-07-25
       
  • Correction to: On efficiency of some restricted estimators in a
           multivariate regression model

    • Free pre-print version: Loading...

      PubDate: 2022-07-23
       
  • Local inhomogeneous second-order characteristics for spatio-temporal point
           processes occurring on linear networks

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      Abstract: Abstract Point processes on linear networks are increasingly being considered to analyse events occurring on particular network-based structures. In this paper, we extend Local Indicators of Spatio-Temporal Association (LISTA) functions to the non-Euclidean space of linear networks, allowing to obtain information on how events relate to nearby events. In particular, we propose the local version of two inhomogeneous second-order statistics for spatio-temporal point processes on linear networks, the K- and the pair correlation functions. We put particular emphasis on the local K-functions, deriving come theoretical results which enable us to show that these LISTA functions are useful for diagnostics of models specified on networks, and can be helpful to assess the goodness-of-fit of different spatio-temporal models fitted to point patterns occurring on linear networks. Our methods do not rely on any particular model assumption on the data, and thus they can be applied for whatever is the underlying model of the process. We finally present a real data analysis of traffic accidents in Medellin (Colombia).
      PubDate: 2022-07-20
       
  • A weighted U-statistic based change point test for multivariate time
           series

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      Abstract: Abstract In this paper we study the change point detection for the mean of multivariate time series. We construct the weighted U-statistic change point tests based on the weight function \(1/{\sqrt{t(1-t)}}\) and some suitable kernel functions. We establish the asymptotic distribution of the test statistic under the null hypothesis and the consistency under the alternatives. A bootstrap procedure is applied to approximate the distribution of the test statistic and it is proved that the test statistic based on bootstrap sampling has the same asymptotic distribution as the original statistic. Numerical simulation and real data analysis show the good performance of the weighted change point test especially when the change point location is not in the middle of the observation period.
      PubDate: 2022-07-19
       
 
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