-
Estimation of the incubation time distribution in the singly and doubly interval censored model Stat. Neerl. (IF 1.5) Pub Date : 2024-02-21 Piet Groeneboom
We analyze nonparametric estimators for the distribution function of the incubation time in the singly and doubly interval censoring model. The classical approach is to use parametric families like Weibull, log‐normal or gamma distributions in the estimation procedure. We propose nonparametric estimates for functions of the observations, which stay closer to the data than the classical parametric methods
-
Point process modeling through a mixture of homogeneous and self-exciting processes Stat. Neerl. (IF 1.5) Pub Date : 2024-01-15 Álvaro Briz-Redón, Jorge Mateu
Self-exciting point processes allow modeling the temporal location of an event of interest, considering the history provided by previously observed events. This family of point processes is commonly used in several areas such as criminology, economics, or seismology, among others. The standard formulation of the self-exciting process implies assuming that the underlying stochastic process is dependent
-
Issue Information Stat. Neerl. (IF 1.5) Pub Date : 2023-12-03
No abstract is available for this article.
-
The concept of sufficiency in conditional frequentist inference Stat. Neerl. (IF 1.5) Pub Date : 2023-12-02 Paul Kabaila, A. H. Welsh
We consider inference about the parameter that determines the distribution of the data. In frequentist inference a very important and useful idea is that data reduction to a sufficient statistic does not lose any information about this parameter. We recall two justifications for this idea in frequentist inference. We then examine the extent to which these justifications carry over to conditional frequentist
-
Marginal log-linear parameters and their collapsibility for categorical data Stat. Neerl. (IF 1.5) Pub Date : 2023-11-13 Sayan Ghosh, P. Vellaisamy
Collapsibility is a practical and useful technique for dimension reduction in multidimensional contingency tables. In this paper, we consider marginal log-linear models for studying collapsibility and related aspects in such tables. These models generalize ordinary log-linear and multivariate logistic models, besides several others. First, we obtain some characteristic properties of marginal log-linear
-
Asymptotic approximations of expectations of power means Stat. Neerl. (IF 1.5) Pub Date : 2023-11-08 Tomislav Burić, Lenka Mihoković
In this paper we study how the expectations of power means behave asymptotically as some relevant parameter approaches infinity and how to approximate them accurately for general nonnegative continuous probability distributions. We derive approximation formulae for such expectations as distribution mean increases, and apply them to some commonly used distributions in statistics and financial mathematics
-
Issue Information Stat. Neerl. (IF 1.5) Pub Date : 2023-10-17
No abstract is available for this article.
-
New article types in Statistica Neerlandica Stat. Neerl. (IF 1.5) Pub Date : 2023-10-17 Ernst-Jan Camiel Wit
In May 2023 a new editorial team, consisting of Edwin van den Heuvel, Veronica Vinciotti and myself, Ernst Wit, currently with the help of Casper Albers, have taken over from our predecessors, Nan van Geloven, Marijtje van Duijn, and Miroslav Ristic. We have immediately moved to a new format, of which we have informed you in the previous issue of Statistica Neerlandica, consisting of a fast-turnaround
-
Editorial statistics Stat. Neerl. (IF 1.5) Pub Date : 2023-10-17 Edwin van den Heuvel, Veronica Vinciotti, Ernst-Jan Camiel Wit
TABLE 1. Editorial statistics 2022. Submissions 2019 2020 2021 2022 2023 (to September 2023) Original articles submitted (by submission year) 94 153 139 142 106 Accepted original articles (by decision year) 28 28 20 19 31 Accept ratio (by final decision year) 27% 19% 15% 23% 19% Impact Factor 1.023 1.190 1.239 1.5 N/A Ranking (Statistics and Probability Journal Citation Reports) 69/124 79/125 82/125
-
Scaling priors for intrinsic Gaussian Markov random fields applied to blood pressure data Stat. Neerl. (IF 1.5) Pub Date : 2023-10-17 Maria-Zafeiria Spyropoulou, James Bentham
An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it
-
Generalized k-variate proportional hazard function for censored survival data Stat. Neerl. (IF 1.5) Pub Date : 2023-10-13 Hilmi Fadel Kittani
This note develops a generalized k $$ k $$ -variate hazard function for censored data in survival analysis. It introduces a generalized recursive formula, extending the bivariate and trivariate cases introduced by Clayton and Cuzick (1985, Journal of the Royal Statistical Society: Series A (General), 148(2):82–108) and Kittani (1995, Journal of Mathematical Sciences, 67–74), respectively. The newly
-
Connections between two classes of estimators for single-index models Stat. Neerl. (IF 1.5) Pub Date : 2023-10-13 Weichao Yang, Xu Guo, Niwen Zhou, Changliang Zou
Single-index model is a very popular and powerful semiparametric model. As an improvement of the maximum rank correlation estimator, Shen et al. proposed the linearized maximum rank correlation estimator. We show that this estimator has some interesting connections with the distribution-transformed least-squares estimator for single-index models. We also propose a rescaled distribution-transformed
-
Asymptotic comparison of negative multinomial and multivariate normal experiments Stat. Neerl. (IF 1.5) Pub Date : 2023-10-13 Christian Genest, Frédéric Ouimet
This note presents a refined local approximation for the logarithm of the ratio between the negative multinomial probability mass function and a multivariate normal density, both having the same mean–covariance structure. This approximation, which is derived using Stirling's formula and a meticulous treatment of Taylor expansions, yields an upper bound on the Hellinger distance between the jittered
-
Forecasting performance of machine learning, time series, and hybrid methods for low- and high-frequency time series Stat. Neerl. (IF 1.5) Pub Date : 2023-09-30 Ozancan Ozdemir, Ceylan Yozgatligil
One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential
-
Testing conditional independence in casual inference for time series data Stat. Neerl. (IF 1.5) Pub Date : 2023-09-19 Zongwu Cai, Ying Fang, Ming Lin, Shengfang Tang
In this paper, we propose a new procedure to test conditional independence assumption in studying casual inference for time series data. The conditional independence assumption is transformed to a nonparametric conditional moment test with the help of auxiliary variables which are allowed to affect policy choice but the dependence can be fully captured by potential outcomes and observable controls
-
An informative prior distribution on functions with application to functional regression Stat. Neerl. (IF 1.5) Pub Date : 2023-09-08 Christophe Abraham
We provide a prior distribution for a functional parameter so that its trajectories are smooth and vanish on a given subset. This distribution can be interpreted as the distribution of an initial Gaussian process conditioned to be zero on a given subset. Precisely, we show that the initial Gaussian process is the sum of the conditioned process and an independent process with probability one and that
-
Semiparametric recovery of central dimension reduction space with nonignorable nonresponse Stat. Neerl. (IF 1.5) Pub Date : 2023-09-06 Siming Zheng, Alan T. K. Wan, Yong Zhou
Sufficient dimension reduction (SDR) methods are effective tools for handling high dimensional data. Classical SDR methods are developed under the assumption that the data are completely observed. When the data are incomplete due to missing values, SDR has only been considered when the data are randomly missing, but not when they are nonignorably missing, which is arguably more difficult to handle
-
The Yates, Conover, and Mantel statistics in 2 × 2 tables revisited (and extended) Stat. Neerl. (IF 1.5) Pub Date : 2023-08-31 Antonio Martín Andrés, María Álvarez Hernández, Francisco Gayá Moreno
Asymptotic inferences about the difference, ratio or odds-ratio of two independent proportions are very common in diverse fields. This article defines for each parameter eight conditional inference methods. These methods depend on: (1) using a chi-squared type statistic or a z type one; (2) using the classic Yates continuity correction or the less well-known Conover one; and (3) whether the p-value
-
Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model Stat. Neerl. (IF 1.5) Pub Date : 2023-08-27 Xinyuan Chen, Denise Esserman, Fan Li
A population-averaged additive subdistribution hazards model is proposed to assess the marginal effects of covariates on the cumulative incidence function and to analyze correlated failure time data subject to competing risks. This approach extends the population-averaged additive hazards model by accommodating potentially dependent censoring due to competing events other than the event of interest
-
Improved estimation of average treatment effects under covariate-adaptive randomization methods Stat. Neerl. (IF 1.5) Pub Date : 2023-08-30 Jun Wang, Yahe Yu
Estimation of the average treatment effect is one of the crucial problems in clinical trials for two or multiple treatments. The covariate-adaptive randomization methods are often applied to balance treatment assignments across prognostic factors in clinical trials, such as the minimization and stratified permuted blocks method. We propose a model-free estimator of average treatment effects under covariate-adaptive
-
Franklin's randomized response model with correlated scrambled variables Stat. Neerl. (IF 1.5) Pub Date : 2023-08-28 Christopher Aguirre-Hamilton, Stephen A. Sedory, Sarjinder Singh
We propose two types of estimators that are analogous to Franklin's model. One estimator is derived by concentrating on the row averages of the responses, and another is obtained by concentrating on the column averages of the observed responses. In the latter case we have two responses per respondent from a bi-variate normal distribution. The proposed estimator based on row averages, by making use
-
A gamma tail statistic and its asymptotics Stat. Neerl. (IF 1.5) Pub Date : 2023-08-17 Toshiya Iwashita, Bernhard Klar
Asmussen and Lehtomaa [Distinguishing log-concavity from heavy tails. Risks 5(10), 2017] introduced an interesting function g $$ g $$ which is able to distinguish between log-convex and log-concave tail behavior of distributions, and proposed a randomized estimator for g $$ g $$ . In this paper, we show that g $$ g $$ can also be seen as a tool to detect gamma distributions or distributions with gamma
-
An efficient automatic clustering algorithm for probability density functions and its applications in surface material classification Stat. Neerl. (IF 1.5) Pub Date : 2023-08-07 Thao Nguyen-Trang, Tai Vo-Van, Ha Che-Ngoc
Clustering is a technique used to partition a dataset into groups of similar elements. In addition to traditional clustering methods, clustering for probability density functions (CDF) has been studied to capture data uncertainty. In CDF, automatic clustering is a clever technique that can determine the number of clusters automatically. However, current automatic clustering algorithms update the new
-
Joint probabilities under expected value constraints, transportation problems, maximum entropy in the mean Stat. Neerl. (IF 1.5) Pub Date : 2023-07-08 Henryk Gzyl, Silvia Mayoral
Here we consider an application of the method of maximum entropy in the mean to solve an extension of the problem of finding a discrete probability distribution from the knowledge of its marginals. The extension consists of determining joint probabilities when, besides specifying the marginals, we specify the expected value of some given random variables. The proposed method can incorporate constraints
-
Issue Information Stat. Neerl. (IF 1.5) Pub Date : 2023-07-02
No abstract is available for this article.
-
Editorial Stat. Neerl. (IF 1.5) Pub Date : 2023-07-02 Ernst C. Wit
Statistica Neerlandica is one of the world's historical statistics journals. It was founded in 1946 with the ambition to cover all areas of statistics, from theoretical to applied, with a special emphasis on mathematical statistics, statistics for the behavioural sciences and biostatistics. It has seen its share of changes over the years, but it stood its ground and continues to serve a role in the
-
Poisson average maximum likelihood-centered penalized estimator: A new estimator to better address multicollinearity in Poisson regression Stat. Neerl. (IF 1.5) Pub Date : 2023-06-22 Sheng Li, Wei Wang, Menghan Yao, Junyu Wang, Qianqian Du, Xuelin Li, Xinyue Tian, Jing Zeng, Ying Deng, Tao Zhang, Fei Yin, Yue Ma
The Poisson ridge estimator (PRE) is a commonly used parameter estimation method to address multicollinearity in Poisson regression (PR). However, PRE shrinks the parameters toward zero, contradicting the real association. In such cases, PRE tends to become an insufficient solution for multicollinearity. In this work, we proposed a new estimator called the Poisson average maximum likelihood-centered
-
Assessing replicability with the sceptical p$$ p $$-value: Type-I error control and sample size planning Stat. Neerl. (IF 1.5) Pub Date : 2023-06-20 Charlotte Micheloud, Fadoua Balabdaoui, Leonhard Held
We study a statistical framework for replicability based on a recently proposed quantitative measure of replication success, the sceptical p $$ p $$ -value. A recalibration is proposed to obtain exact overall Type-I error control if the effect is null in both studies and additional bounds on the partial and conditional Type-I error rate, which represent the case where only one study has a null effect
-
A case study of Gulf Securities Market in the last 20 years: A Long Short-Term Memory approach Stat. Neerl. (IF 1.5) Pub Date : 2023-06-07 Abhibasu Sen, Karabi Dutta Choudhury
Various researches have been conducted on forecasting stock prices. Several tools ranging from statistical techniques to quantitative methods have been used by researchers to forecast the market. But so far, very little research has been done on forecasting the stock markets of the Gulf countries such as Saudi Arabia, United Arab Emirates, Oman, Kuwait, Bahrain, and Qatar. Our approach is to predict
-
The analysis of semi-competing risks data using Archimedean copula models Stat. Neerl. (IF 1.5) Pub Date : 2023-06-01 Antai Wang, Ziyan Guo, Yilong Zhang, Jihua Wu
In this paper, we derive the copula-graphic estimator (Zheng and Klein) for marginal survival functions using Archimedean copula models based on competing risks data subject to univariate right censoring and prove its uniform consistency and asymptotic properties. We then propose a novel parameter estimation method based on the semi-competing risks data using Archimedean copula models. Based on our
-
Robust Liu-type estimator based on GM estimator Stat. Neerl. (IF 1.5) Pub Date : 2023-05-29 Melike Işılar, Y. Murat Bulut
Ordinary Least Squares Estimator (OLSE) is widely used to estimate parameters in regression analysis. In practice, the assumptions of regression analysis are often not met. The most common problems that break these assumptions are outliers and multicollinearity problems. As a result of these problems, OLSE loses efficiency. Therefore, alternative estimators to OLSE have been proposed to solve these
-
On partially observed competing risks model for Chen distribution under generalized progressive hybrid censoring Stat. Neerl. (IF 1.5) Pub Date : 2023-05-22 Kundan Singh, Amulya Kumar Mahto, Yogesh Mani Tripathi
In this paper, we discuss the inference for the competing risks model when the failure times follow Chen distribution. With assumption of two causes of failures, which are partially observed, are considered as independent. The existence and uniqueness of maximum likelihood estimates for model parameters are obtained under generalized progressive hybrid censoring. Also, we discussed the classical and
-
Testing for jumps with robust spot volatility estimators Stat. Neerl. (IF 1.5) Pub Date : 2023-05-19 Yucheng Sun
Jumps in the paths of efficient asset prices have important economic implications. Motivated by the issue of testing for jumps based on noisy high-frequency data, we develop a novel spot volatility estimator, which is obtained by minimizing the sum of some Huber loss functions, and use it as an ingredient for jump detection. This type of estimators is uniformly consistent in estimating the spot volatilities
-
Orthogonal contrasts for both balanced and unbalanced designs and both ordered and unordered treatments Stat. Neerl. (IF 1.5) Pub Date : 2023-05-16 J. C. W. Rayner, G. C. Livingston
We consider designs with t treatments, the ith level of which has ni observations. Four cases are examined: treatment levels both ordered and not, and the design balanced, with all ni equal, and not. A general construction is given that takes observations, typically treatment sums or treatment rank sums, constructs a simple quadratic form and expresses it as a sum of squares of orthogonal contrasts
-
Linear regression models with multiplicative distortions under new identifiability conditions Stat. Neerl. (IF 1.5) Pub Date : 2023-05-11 Jun Zhang, Bingqing Lin, Yan Zhou
This paper considers linear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. We propose new identifiability conditions for the distortion functions via the varying coefficient models, then moment-based estimators of parameters in the model are proposed by using the estimated varying
-
New closed-form efficient estimator for multivariate gamma distribution Stat. Neerl. (IF 1.5) Pub Date : 2023-04-28 Yu-Hyeong Jang, Jun Zhao, Hyoung-Moon Kim, Kyusang Yu, Sunghoon Kwon, SungHwan Kim
Maximum likelihood estimation is used widely in classical statistics. However, except in a few cases, it does not have a closed form. Furthermore, it takes time to derive the maximum likelihood estimator (MLE) owing to the use of iterative methods such as Newton–Raphson. Nonetheless, this estimation method has several advantages, chief among them being the invariance property and asymptotic normality
-
The optimal input-independent baseline for binary classification: The Dutch Draw Stat. Neerl. (IF 1.5) Pub Date : 2023-04-25 Joris Pries, Etienne van de Bijl, Jan Klein, Sandjai Bhulai, Rob van der Mei
Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frame of reference given by a baseline method, it is impossible to determine if a score is “good” or “bad.” The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the “best” and why. By identifying
-
The multilateral spatial integer-valued process of order 1 Stat. Neerl. (IF 1.5) Pub Date : 2023-04-25 Dimitris Karlis, Azmi Chutoo, Naushad Mamode Khan, Vandna Jowaheer
In spatial count data analysis, modeling with a multilateral lattice structure presents some important challenges. They include both the model construction and the estimation of the model parameters, since the structure accommodates the left, right, top, bottom, and diagonal site effects. Thus, the multilateral spatial process unifies all the popular spatial subclasses that include the unilateral,
-
Stochastic comparisons of largest claim amounts from heterogeneous portfolios Stat. Neerl. (IF 1.5) Pub Date : 2023-04-20 Pradip Kundu, Amarjit Kundu, Biplab Hawlader
This paper investigates stochastic comparisons of largest claim amounts of two sets of independent or interdependent portfolios in the sense of some stochastic orders. Let random variable X i $$ {X}_i $$ ( i = 1 , … , n $$ i=1,\dots, n $$ ) with distribution function F ( x ; α i ) $$ F\left(x;{\alpha}_i\right) $$ , represents the claim amount for ith risk of a portfolio. Here two largest claim amounts
-
Analysis of cross-over experiments with count data in the presence of carry-over effects Stat. Neerl. (IF 1.5) Pub Date : 2023-04-16 Nelson Alirio Cruz, Luis Alberto López Pérez, Oscar Orlando Melo
This paper presents an experimental cross-over design whose response variable is a count that belongs to the Poisson distribution. The methodology is extended to data with overdispersion or subdispersion. We present the theoretical development for analysis of cases with few treatments and a few periods. In this case, we consider the log-linear link for estimation effects and the Delta method for the
-
Issue Information Stat. Neerl. (IF 1.5) Pub Date : 2023-04-02
No abstract is available for this article.
-
Estimating function method for nonnegative autoregressive models Stat. Neerl. (IF 1.5) Pub Date : 2023-03-31 E. Hari Prasad, N. Balakrishna
A stationary sequence of nonnegative random variables generated by autoregressive (AR) models may be used to describe the inter-arrival times between events in counting processes. Even though, several such models are available in the literature, there is no unified approach to estimate their parameters. In this paper, we propose a class of combined estimating function method to estimate the model parameters
-
A robust mixed-effects parametric quantile regression model for continuous proportions: Quantifying the constraints to vitality in cushion plants Stat. Neerl. (IF 1.5) Pub Date : 2023-03-29 Divan A. Burger, Sean van der Merwe, Emmanuel Lesaffre, Peter C. le Roux, Morgan J. Raath-Krüger
There is no literature on outlier-robust parametric mixed-effects quantile regression models for continuous proportion data as an alternative to systematically identifying and eliminating outliers. To fill this gap, we formulate a robust method by extending the recently proposed fixed-effects quantile regression model based on the heavy-tailed Johnson- t $$ t $$ distribution for continuous proportion
-
Logistic or not Logistic? Stat. Neerl. (IF 1.5) Pub Date : 2023-03-27 James S. Allison, Bruno Ebner, Marius Smuts
We propose a new class of goodness-of-fit tests for the logistic distribution based on a characterization related to the density approach in the context of Stein's method. This characterization-based test is a first of its kind for the logistic distribution. The asymptotic null distribution of the test statistic is derived and it is shown that the test is consistent against fixed alternatives. The
-
A partial posterior p value test for multilevel mediation Stat. Neerl. (IF 1.5) Pub Date : 2023-03-16 Kyle Cox, Benjamin Kelcey
A variety of inferential tests are available for single and multilevel mediation but most come with notable limitations that balance tradeoffs between power and Type I error. We extend the partial posterior p value method (p3 method) to test multilevel mediation. This contemporary resampling-based composite approach is specifically suited for complex null hypotheses. We develop the p3 method and investigate
-
A portmanteau test for the iid hypothesis Stat. Neerl. (IF 1.5) Pub Date : 2023-02-28 Ricardo Bórquez
In this paper, we introduce a new portmanteau test for the iid hypothesis, where the elements of the sample are allowed to take values in a general space (e.g., a function space). We study the finite sample properties of our test, evaluating its performance in terms of empirical size and power. In particular, we compare the empirical power of our test with the power of other tests in the literature
-
Bayesian solution to the monotone likelihood in the standard mixture cure model Stat. Neerl. (IF 1.5) Pub Date : 2023-02-26 Frederico M. Almeida, Vinícius D. Mayrink, Enrico A. Colosimo
An advantage of the standard mixture cure model over an usual survival model is how it accounts for the population heterogeneity. It allows a joint estimation for the distribution related to the susceptible and non-susceptible subjects. The estimation algorithm may provide ± ∞ $$ \pm \infty $$ coefficients when the likelihood cannot be maximized. This phenomenon is known as Monotone Likelihood (ML)
-
Testing the common risk difference of proportions for stratified uni- and bilateral correlated data Stat. Neerl. (IF 1.5) Pub Date : 2023-02-16 Zhiming Li, Changxing Ma, Keyi Mou
In medical clinical studies, uni- and bilateral data naturally occurs if each patient contributes either one or both of paired organ measurements in a stratified design. This paper mainly proposes a common test of risk differences between proportions for stratified uni- and bilateral correlated data. Likelihood ratio, score, and Wald-type test statistics are constructed using global, unconstrained
-
Inference in the presence of likelihood monotonicity for proportional hazards regression Stat. Neerl. (IF 1.5) Pub Date : 2023-01-20 John E. Kolassa, Juan Zhang
Proportional hazards are often used to model event time data subject to censoring. Samples involving discrete covariates with strong effects can lead to infinite maximum partial likelihood estimates. A methodology is presented for eliminating nuisance parameters estimated at infinity using approximate conditional inference. Of primary interest is testing in cases in which the parameter of primary interest
-
Estimating random effects in a finite Markov chain with absorbing states: Application to cognitive data Stat. Neerl. (IF 1.5) Pub Date : 2023-01-19 Pei Wang, Erin L. Abner, Changrui Liu, David W. Fardo, Frederick A. Schmitt, Gregory A. Jicha, Linda J. Van Eldik, Richard J. Kryscio
Finite Markov chains with absorbing states are popular tools for analyzing longitudinal data with categorical responses. The one step transition probabilities can be defined in terms of fixed and random effects but it is difficult to estimate these effects due to many unknown parameters. In this article we propose a three-step estimation method. In the first step the fixed effects are estimated by
-
Discretized skew-t mixture model for deconvoluting liquid chromatograph mass spectrometry data Stat. Neerl. (IF 1.5) Pub Date : 2023-01-13 Xuwen Zhu, Xiang Zhang
In this paper, new statistical algorithms for accurate peak detection in the metabolomic data are proposed. Specifically, liquid chromatograph-mass spectrometry data are analyzed. The discretized skew-t mixture model for peak detection is proposed. It shows great flexibility and capability in fitting skewed or heavy-tailed peaks. The methodology is further extended to cross-sample peak alignment for
-
Asymptotic properties of nonparametric quantile estimation with spatial dependency Stat. Neerl. (IF 1.5) Pub Date : 2022-11-10 S.-H. Arnaud Kanga, Ouagnina Hili, Sophie Dabo-Niang, Assi N'Guessan
The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of
-
Prior effective sample size in phase II clinical trials with mixed binary and continuous responses Stat. Neerl. (IF 1.5) Pub Date : 2022-11-07 Meghna Bose, Jean-François Angers, Atanu Biswas
The problem of finding Effective Sample Size (ESS) in Phase II clinical trials where toxicity and efficacy are the two components of the treatment response vector is considered. In particular, one of the components is assumed to be binary and the other is assumed to be continuous. The case of binary safety and continuous efficacy is studied for different prior distributions under different set up.
-
Inference for log-location-scale family of distributions under competing risks with progressive type-I interval censored data Stat. Neerl. (IF 1.5) Pub Date : 2022-11-02 Soumya Roy, Biswabrata Pradhan
In this article, we present statistical inference of unknown lifetime parameters based on a progressive Type-I interval censored dataset in presence of independent competing risks. A progressive Type-I interval censoring scheme is a generalization of an interval censoring scheme, allowing intermediate withdrawals of test units at the inspection points. We assume that the lifetime distribution corresponding
-
Bayesian inference for a mixture double autoregressive model Stat. Neerl. (IF 1.5) Pub Date : 2022-10-28 Kai Yang, Qingqing Zhang, Xinyang Yu, Xiaogang Dong
This paper considers a mixture double autoregressive model with two components, which can flexibly capture the features usually exhibited by many financial returns such as heteroscedasticity, large kurtosis and multimodal marginals. Bayesian method based on modern Markov Chain Monte Carlo (MCMC) technology is used to estimate the model parameters. The heteroscedasticity test problem for the underlying
-
Issue Information Stat. Neerl. (IF 1.5) Pub Date : 2022-10-05
No abstract is available for this article.
-
Editorial Statistics Stat. Neerl. (IF 1.5) Pub Date : 2022-10-05 Miroslav Ristic, Marijtje van Duijn, Nan van Geloven
TABLE 1. Editorial statistics 2022 Submissions 2018 2019 2020 2021 2022 (to September 2022) Original articles submitted (by submission year) 94 94 153 139 107 Accepted original articles (by decision year) 26 28 28 20 13 Accept ratio (by final decision year) 38% 27% 19% 15% 22% Impact factor 0.433 1.023 1.190 1.239 N/A Ranking (Statistics and Probability Journal Citation Reports) 117/123 69/124 79/125
-
A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise Stat. Neerl. (IF 1.5) Pub Date : 2022-09-26 Julia Calatayud, Marc Jornet, Jorge Mateu
We model the incidence of the COVID-19 disease during the first wave of the epidemic in Castilla-Leon (Spain). Within-province dynamics may be governed by a generalized logistic map, but this lacks of spatial structure. To couple the provinces, we relate the daily new infections through a density-independent parameter that entails positive spatial correlation. Pointwise values of the input parameters
-
A discrete truncated Zipf distribution Stat. Neerl. (IF 1.5) Pub Date : 2022-09-26 Kwame Boamah-Addo, Tomasz J. Kozubowski, Anna K. Panorska
We provide a comprehensive account of fundamental properties of a truncated discrete Zipf distribution, complementing the results available in the literature. In particular, we obtain results on existence and uniqueness of maximum likelihood parameter estimators and propose new testing methodology for the shape parameter. We also include data examples illustrating applicability of this stochastic model
-
Testing for differences in chain equating Stat. Neerl. (IF 1.5) Pub Date : 2022-07-22 Michela Battauz
The comparability of the scores obtained in different forms of a test is certainly an essential requirement. This paper proposes a statistical test for the detection of noncomparable scores based on item response theory (IRT) methods. When the IRT model is fit separately for different forms of a test, the item parameter estimates are expressed on different measurement scales. The first step to obtain