Journal of Applied Statistics Impact Factor & Information

Publisher: Taylor & Francis (Routledge)

Journal description

Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees. Each issue aims for a balance of methodological innovation, thorough evaluation of existing techniques, case studies, speculative articles, book reviews and letters. Gopal Kanji, the Editor, has been running the Journal of Applied Statistics for 25 years in 1998. Journal of Applied Statistics includes a supplement on Advances in Applied Statistics. Each annual edition of the supplement aims to provide a comprehensive and modern account of a subject at the cutting edge of applied statistics. Individual articles and entire thematic issues are invited and commissioned from authors in the forefront of their speciality, linking established themes to current and future developments.

Current impact factor: 0.42

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 0.417
2013 Impact Factor 0.453
2012 Impact Factor 0.449
2011 Impact Factor 0.405
2010 Impact Factor 0.306
2009 Impact Factor 0.407
2008 Impact Factor 0.28
2007 Impact Factor 0.222
2006 Impact Factor 0.48
2005 Impact Factor 0.306
2004 Impact Factor 0.665
2003 Impact Factor 0.597
2002 Impact Factor 0.265
2001 Impact Factor 0.296
2000 Impact Factor 0.206
1999 Impact Factor 0.257
1998 Impact Factor 0.316
1997 Impact Factor 0.448

Impact factor over time

Impact factor

Additional details

5-year impact 0.59
Cited half-life >10.0
Immediacy index 0.07
Eigenfactor 0.00
Article influence 0.29
Website Journal of Applied Statistics website
Other titles Journal of applied statistics (Online)
ISSN 0266-4763
OCLC 48215794
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Taylor & Francis (Routledge)

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Some individual journals may have policies prohibiting pre-print archiving
    • On author's personal website or departmental website immediately
    • On institutional repository or subject-based repository after either 12 months embargo
    • Publisher's version/PDF cannot be used
    • On a non-profit server
    • Published source must be acknowledged
    • Must link to publisher version
    • Set statements to accompany deposits (see policy)
    • The publisher will deposit in on behalf of authors to a designated institutional repository including PubMed Central, where a deposit agreement exists with the repository
    • STM: Science, Technology and Medicine
    • Publisher last contacted on 25/03/2014
    • This policy is an exception to the default policies of 'Taylor & Francis (Routledge)'
  • Classification

Publications in this journal

  • Journal of Applied Statistics 11/2015; DOI:10.1080/02664763.2015.1103708
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we study the statistical inference based on the Bayesian approach for regression models with the assumption that independent additive errors follow normal, Student-t, slash, contaminated normal, Laplace or symmetric hyperbolic distribution, where both location and dispersion parameters of the response variable distribution include nonparametric additive components approximated by B-splines. This class of models provides a rich set of symmetric distributions for the model error. Some of these distributions have heavier or lighter tails than the normal as well as different levels of kurtosis. In order to draw samples of the posterior distribution of the interest parameters, we propose an efficient Markov Chain Monte Carlo (MCMC) algorithm, which combines Gibbs sampler and Metropolis–Hastings algorithms. The performance of the proposed MCMC algorithm is assessed through simulation experiments. We apply the proposed methodology to a real data set. The proposed methodology is implemented in the R package BayesGESM using the function gesm().
    Journal of Applied Statistics 11/2015; DOI:10.1080/02664763.2015.1109070
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper provides methods of obtaining Bayesian D-optimal Accelerated Life Test (ALT) plans for series systems with independent exponential component lives under the Type-I censoring scheme. Two different Bayesian D-optimality design criteria are considered. For both the criteria, first optimal designs for a given number of experimental points are found by solving a finite-dimensional constrained optimization problem. Next, the global optimality of such an ALT plan is ensured by applying the General Equivalence Theorem. A detailed sensitivity analysis is also carried out to investigate the effect of different planning inputs on the resulting optimal ALT plans. Furthermore, these Bayesian optimal plans are also compared with the corresponding (frequentist) locally D-optimal ALT plans.
    Journal of Applied Statistics 11/2015; DOI:10.1080/02664763.2015.1106449
  • [Show abstract] [Hide abstract]
    ABSTRACT: Within the context of the period fixed-effects model, this study uses a 2002–2009 state-level panel data set of the USA to investigate the relative impact of state cigarette excise taxation across the nation in reducing cigarette smoking. In particular, by focusing upon the state cigarette excise taxation levels within each of the nine US Census Divisions, this study investigates whether there are inter-regional differences in the rate of responsiveness of cigarette consumption to increased state cigarette taxes. The initial empirical estimates reveal that although the per capita number of packs of cigarettes smoked annually is a decreasing function of the state cigarette excise tax in all nine Census Regions, the relative response of cigarette smoking to state cigarette tax increases varies considerably from one region to the next. Reinforcing this conclusion, in one specification of the model, the number of packs of cigarettes smoked in response to a higher state cigarette tax is statistically significant and negative in only eight of the nine Census Divisions. Furthermore, when cigarette smoking is measured in terms of the percentage of the population classified as smokers, interregional differentials in the response of smokers to higher state cigarette taxes are much greater. Thus, there is evidence that cigarette excise taxation exercises rather different impacts on the propensity to smoke across Census Regions.
    Journal of Applied Statistics 11/2015; DOI:10.1080/02664763.2015.1106451
  • [Show abstract] [Hide abstract]
    ABSTRACT: A hurdle in the peaks-over-threshold approach for analyzing extreme values is the selection of the threshold. A method is developed to reduce this obstacle in the presence of multiple, similar data samples. This is for instance the case in many environmental applications. The idea is to combine threshold selection methods into a regional method. Regionalized versions of the threshold stability and the mean excess plot are presented as graphical tools for threshold selection. Moreover, quantitative approaches based on the bootstrap distribution of the spatially averaged Kolmogorov–Smirnov and Anderson–Darling test statistics are introduced. It is demonstrated that the proposed regional method leads to an increased sensitivity for too low thresholds, compared to methods that do not take into account the regional information. The approach can be used for a wide range of univariate threshold selection methods. We test the methods using simulated data and present an application to rainfall data from the Dutch water board Vallei en Veluwe.
    Journal of Applied Statistics 11/2015; DOI:10.1080/02664763.2015.1100589
  • [Show abstract] [Hide abstract]
    ABSTRACT: Classification is a data mining technique that aims to discover a model from training data that distinguishes records into appropriate classes. Classification methods can be applied in education, to classify non-active students in higher education programs based on their characteristics. This paper presents a comparison of three classification methods: Naïve Bayes, Bagging, and C4.5. The criteria used to evaluate performance of three classifiers are stratified cross-validation, confusion matrix, ROC curve, recall, precision, and F-measure. The data used for this paper are non-active students in Indonesia Open University (IOU) for the period of 2004–2012. The non-active students were divided into three groups: non-active students in the first three years, non-active students in first five years, and non-active students over five years. Results of the study show that the Bagging method provided a higher accuracy than Naïve Bayes and C4.5. The accuracy of bagging classification is 82.99%, while the Naïve Bayes and C4.5 are 80.04% and 82.74%, respectively. The classification tree resulted from the Bagging method has a large number of nodes, so it is quite difficult to use in decision-making. For that, the C4.5 tree is used to classify non-active students in IOU based in their characteristics.
    Journal of Applied Statistics 11/2015; DOI:10.1080/02664763.2015.1077940
  • [Show abstract] [Hide abstract]
    ABSTRACT: Non-inferiority tests are often measured for the diagnostic accuracy in medical research. The area under the receiver operating characteristic (ROC) curve is a familiar diagnostic measure for the overall diagnostic accuracy. Nevertheless, since it may not differentiate the diverse shapes of the ROC curves with different diagnostic significance, the partial area under the ROC (PAUROC) curve, another summary measure emerges for such diagnostic processes that require the false-positive rate to be in the clinically interested range. Traditionally, to estimate the PAUROC, the golden standard (GS) test on the true disease status is required. Nevertheless, the GS test may sometimes be infeasible. Besides, in a lot of research fields such as the epidemiology field, the true disease status of the patients may not be known or available. Under the normality assumption on diagnostic test results, based on the expectation-maximization algorithm in combination with the bootstrap method, we propose the heuristic method to construct a non-inferiority test for the difference in the paired PAUROCs without the GS test. Through the simulation study, although the proposed method might provide a liberal test, as a whole, the empirical size of the proposed method sufficiently controls the size at the significance level, and the empirical power of the proposed method in the absence of the GS is as good as that of the non-inferiority in the presence of the GS. The proposed method is illustrated with the published data.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1070810
  • [Show abstract] [Hide abstract]
    ABSTRACT: We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1100590
  • [Show abstract] [Hide abstract]
    ABSTRACT: For surveys with sensitive questions, randomized response sampling strategies are often used to increase the response rate and encourage participants to provide the truth of the question while participants' privacy and confidentiality are protected. The proportion of responding ‘yes’ to the sensitive question is the parameter of interest. Asymptotic confidence intervals for this proportion are calculated from the limiting distribution of the test statistic, and are traditionally used in practice for statistical inference. It is well known that these intervals do not guarantee the coverage probability. For this reason, we apply the exact approach, adjusting the critical value as in [10], to construct the exact confidence interval of the proportion based on the likelihood ratio test and three Wilson-type tests. Two randomized response sampling strategies are studied: the Warner model and the unrelated model. The exact interval based on the likelihood ratio test has shorter average length than others when the probability of the sensitive question is low. Exact Wilson intervals have good performance in other cases. A real example from a survey study is utilized to illustrate the application of these exact intervals.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1094454
  • [Show abstract] [Hide abstract]
    ABSTRACT: Control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger disturbances in the process parameters, whereas cumulative sum (CUSUM) and exponential weighted moving average (EWMA) are meant for smaller and moderate changes. In this study, we enhanced mixed EWMA–CUSUM control charts with varying fast initial response (FIR) features and also with a runs rule of two out of three successive points that fall above the upper control limit. We investigate their run-length properties. The proposed control charting schemes are compared with the existing counterparts including classical CUSUM, classical EWMA, FIR CUSUM, FIR EWMA, mixed EWMA–CUSUM, 2/3 modified EWMA, and 2/3 CUSUM control charting schemes. A case study is presented for practical considerations using a real data set.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1094453
  • [Show abstract] [Hide abstract]
    ABSTRACT: The problem of testing the similarity of two normal populations is reconsidered, in this article, from a nonclassical point of view. We introduce a test statistic based on the maximum likelihood estimate of Weitzman's overlapping coefficient. Simulated critical points are provided for the proposed test for various sample sizes and significance levels. Statistical powers of the proposed test are computed via simulation studies and compared to those of the existing tests. Furthermore, Type-I error robustness of the proposed and the existing tests are studied via simulation studies when the underlying distributions are non-normal. Two data sets are analyzed for illustration purposes. Finally, the proposed test has been implemented to assess the bioequivalence of two drug formulations.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1100591
  • [Show abstract] [Hide abstract]
    ABSTRACT: Classical time-series theory assumes values of the response variable to be ‘crisp’ or ‘precise’, which is quite often violated in reality. However, forecasting of such data can be carried out through fuzzy time-series analysis. This article presents an improved method of forecasting based on L–R fuzzy sets as membership functions. As an illustration, the methodology is employed for forecasting India's total foodgrain production. For the data under consideration, superiority of proposed method over other competing methods is demonstrated in respect of modelling and forecasting on the basis of mean square error and average relative error criteria. Finally, out-of-sample forecasts are also obtained.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1092111
  • [Show abstract] [Hide abstract]
    ABSTRACT: Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA–CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA–CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA–CMI–OI, for inferring GRNs. After imputation of missing data, the performances of PCA–CMI and PCA–CMI–OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA–CMI–OI algorithm outperform other imputation methods and PCA–CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1079307
  • [Show abstract] [Hide abstract]
    ABSTRACT: Mixture of linear regression models provide a popular treatment for modeling nonlinear regression relationship. The traditional estimation of mixture of regression models is based on Gaussian error assumption. It is well known that such assumption is sensitive to outliers and extreme values. To overcome this issue, a new class of finite mixture of quantile regressions (FMQR) is proposed in this article. Compared with the existing Gaussian mixture regression models, the proposed FMQR model can provide a complete specification on the conditional distribution of response variable for each component. From the likelihood point of view, the FMQR model is equivalent to the finite mixture of regression models based on errors following asymmetric Laplace distribution (ALD), which can be regarded as an extension to the traditional mixture of regression models with normal error terms. An EM algorithm is proposed to obtain the parameter estimates of the FMQR model by combining a hierarchical representation of the ALD. Finally, the iterated weighted least square estimation for each mixture component of the FMQR model is derived. Simulation studies are conducted to illustrate the finite sample performance of the estimation procedure. Analysis of an aphid data set is used to illustrate our methodologies.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1094035
  • [Show abstract] [Hide abstract]
    ABSTRACT: The paper is devoted to explore how the increasing availability of spatial micro-data, jointly with the diffusion of GIS software, allows to exploit micro-econometric methods based on stochastic spatial point processes in order to understand the factors that may influence the location decisions of new firms. By using the knowledge of the geographical coordinates of the newborn firms, their spatial distribution is treated as a realization of an inhomogeneous marked point process in the continuous space and the effect of spatial-varying factors on the location decisions is evaluated by parametrically modelling the intensity of the process. The study is motivated by the real issue of analysing the birth process of small and medium manufacturing firms in Tuscany, an Italian region, and it shows that the location choices of the new Tuscan firms is influenced on the one hand by the availability of infrastructures and the level of accessibility, and on the other by the presence and the characteristics of the existing firms. Moreover, the effect of these factors varies with the size and the level of technology of the new firms. Besides the specific Tuscan result, the study shows the potentiality of the described micro-econometric approach for the analysis of the spatial dynamics of firms.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1093612
  • [Show abstract] [Hide abstract]
    ABSTRACT: Functional boxplot is an attractive technique to visualize data that come from functions. We propose an alternative to the functional boxplot based on depth measures. Our proposal generalizes the usual construction of the box-plot in one dimension related to the down-upward orderings of the data by considering two intuitive pre-orders in the functional context. These orderings are based on the epigraphs and hypographs of the data that allow a new definition of functional quartiles which is more robust to shape outliers. Simulated and real examples show that this proposal provides a convenient visualization technique with a great potential for analyzing functional data and illustrate its usefulness to detect outliers that other procedures do not detect.
    Journal of Applied Statistics 10/2015; DOI:10.1080/02664763.2015.1092108