Journal of Applied Statistics (J APPL STAT)

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: 0.45

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 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
Year

Additional details

5-year impact 0.53
Cited half-life 0.00
Immediacy index 0.07
Eigenfactor 0.00
Article influence 0.32
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
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Biclustering is the simultaneous clustering of two related dimensions, for example, of individuals and features, or genes and experimental conditions. Very few statistical models for biclustering have been proposed in the literature. Instead, most of the research has focused on algorithms to find biclusters. The models underlying them have not received much attention. Hence, very little is known about the adequacy and limitations of the models and the efficiency of the algorithms. In this work, we shed light on associated statistical models behind the algorithms. This allows us to generalize most of the known popular biclustering techniques, and to justify, and many times improve on, the algorithms used to find the biclusters. It turns out that most of the known techniques have a hidden Bayesian flavor. Therefore, we adopt a Bayesian framework to model biclustering. We propose a measure of biclustering complexity (number of biclusters and overlapping) through a penalized plaid model, and present a suitable version of the deviance information criterion to choose the number of biclusters, a problem that has not been adequately addressed yet. Our ideas are motivated by the analysis of gene expression data.
    Journal of Applied Statistics 06/2015; 42(6). DOI:10.1080/02664763.2014.999647
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study analyzed the time–frequency relationship between oil price and exchange rate for Pakistan by using measures of continuous wavelet such as wavelet power, cross-wavelet power, and cross-wavelet coherency (WTC). The results of cross-wavelet analysis indicated that covariance between oil price and exchange rate is unable to give clear-cut results, but both variables have been in phase and out phase (i.e. they are anti-cyclical and cyclical in nature) in some or other durations. However, results of squared wavelet coherence disclose that both variables are out of phase and real exchange rate was leading during the entire period studied, corresponding to the 10–15 months’ scale. These results are the unique contribution of the present study, which would have not been drawn if one would have utilized any other time series or frequency domain-based approach. This finding provides evidence of anti-cyclical relationship between oil price and real effective exchange rate; however, in most of the period studied, real exchange rate was leading and passing anti-cycle effects on oil price shocks which is the major contribution of the study.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.980784
  • [Show abstract] [Hide abstract]
    ABSTRACT: In the current study, a new method by the weighting absolute centered external variable (WCEV) was proposed to stabilize heteroscedasticity for butterfly-distributed residuals (BDRs). After giving brief information about heteroscedasticity and BDRs, WCEV was introduced. The WCEV and commonly used variance stabilizing methods are compared on a simple and a multiple regression model. The WCEV was also tested for other type of heteroscedasticity patterns. In addition to heteroscedasticity, other regression assumptions were checked for the WCEV.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.980791
  • [Show abstract] [Hide abstract]
    ABSTRACT: As the metropolitan city in Western China, Chengdu has been suffered from serious traffic congestion. The strategy of urban public transport priority was put into agenda to relieve traffic congestion. But the public transport sharing rate is only 27% in Chengdu which is much lower than the developed country. Consequently, it is of great importance to study the measures to improve the service, and provide technical support to the policy-makers. This paper selected the traffic corridor between Southwest Jiaotong University district and downtown as the experiment subject. The orthogonal design was used to generate stated preference questionnaires in order to achieve the reliable parameter estimates. Some variables were used to define the utility of the three alternatives and construct the Logit model. Then, the relationships between the cost, time variable and the choice probability of the public transport were analyzed. According to the results, we found that the orthogonal design does improve the goodness-of-fit. The workability of Multinomial Logit Model was better than Nest Logit model. We also put forward some effective measures to improve the service level of public transit, including reducing the access time to Metro, limiting parking supply to control the car use.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.986438
  • [Show abstract] [Hide abstract]
    ABSTRACT: Climate is an essential component in site suitability for agriculture in general, and specifically in viticulture. With the recent increase in vineyards on the East Coast, an important climactic consideration in site suitability is extreme winter temperature. Often, maps of annual minimum temperatures are used to determine cold hardiness. However, cold hardiness of grapes is a more complicated process, since the temperature that grapes are able to withstand without damage is not constant. Rather, recent temperature cause acclimation or deacclimation and hence, have a large influence on cold hardiness. By combining National Oceanic and Atmospheric Administration (NOAA) weather station data and leveraging recently created cold hardiness models for grapes, we develop a dynamic spatio-temporal model to determine the risk of winter damage due to extreme cold for several grape varieties commonly grown in the eastern United States. This analysis provides maps of winter damage risk to three grape varieties, Chardonnay, Cabernet Sauvignon, and Concord.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.987652
  • [Show abstract] [Hide abstract]
    ABSTRACT: ‘Middle censoring’ is a very general censoring scheme where the actual value of an observation in the data becomes unobservable if it falls inside a random interval (L, R) and includes both left and right censoring. In this paper, we consider discrete lifetime data that follow a geometric distribution that is subject to middle censoring. Two major innovations in this paper, compared to the earlier work of Davarzani and Parsian [3], include (i) an extension and generalization to the case where covariates are present along with the data and (ii) an alternate approach and proofs which exploit the simple relationship between the geometric and the exponential distributions, so that the theory is more in line with the work of Iyer et al. [6]. It is also demonstrated that this kind of discretization of life times gives results that are close to the original data involving exponential life times. Maximum likelihood estimation of the parameters is studied for this middle-censoring scheme with covariates and their large sample distributions discussed. Simulation results indicate how well the proposed estimation methods work and an illustrative example using time-to-pregnancy data from Baird and Wilcox [1] is included.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.993364
  • [Show abstract] [Hide abstract]
    ABSTRACT: Traditional factor analysis (FA) rests on the assumption of multivariate normality. However, in some practical situations, the data do not meet this assumption; thus, the statistical inference made from such data may be misleading. This paper aims at providing some new tools for the skew-normal (SN) FA model when missing values occur in the data. In such a model, the latent factors are assumed to follow a restricted version of multivariate SN distribution with additional shape parameters for accommodating skewness. We develop an analytically feasible expectation conditional maximization algorithm for carrying out parameter estimation and imputation of missing values under missing at random mechanisms. The practical utility of the proposed methodology is illustrated with two real data examples and the results are compared with those obtained from the traditional FA counterparts.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.986437
  • Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.989467
  • [Show abstract] [Hide abstract]
    ABSTRACT: For square contingency tables with ordered categories, there may be some cases that one wants to analyze them by considering collapsed tables with some adjacent categories combined in the original table. This paper considers the symmetry model for collapsed square contingency tables and proposes a measure to represent the degree of departure from symmetry. The proposed measure is defined as the arithmetic mean of submeasures each of which represents the degree of departure from symmetry for each collapsed 3×3 table. Each submeasure also represents the mean of power-divergence or diversity index for each collapsed table. Examples are given.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.993362
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article considers the uncertainty of a proportion based on a stratified random sample of a small population. Using the hypergeometric distribution, a Clopper–Pearson type upper confidence bound is presented. Another frequentist approach that uses the estimated variance of the proportion estimator is also considered as well as a Bayesian alternative. These methods are demonstrated with an illustrative example. Some aspects of planning, that is, the impact of specified strata sample sizes, on uncertainty are studied through a simulation study.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.987651
  • [Show abstract] [Hide abstract]
    ABSTRACT: The dynamic Nelson–Siegel (DNS) model and even the Svensson generalization of the model have trouble in fitting the short maturity yields and fail to grasp the characteristics of the Japanese government bonds yield curve, which is flat at the short end and has multiple inflection points. Therefore, a closely related generalized dynamic Nelson–Siegel (GDNS) model that has two slopes and curvatures is considered and compared empirically to the traditional DNS in terms of in-sample fit as well as out-of-sample forecasts. Furthermore, the GDNS with time-varying volatility component, modeled as standard EGARCH process, is also considered to evaluate its performance in relation to the GDNS. The GDNS model unanimously outperforms the DNS in terms of in-sample fit as well as out-of-sample forecasts. Moreover, the extended model that accounts for time-varying volatility outpace the other models for fitting the yield curve and produce relatively more accurate 6- and 12-month ahead forecasts, while the GDNS model comes with more precise forecasts for very short forecast horizons.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.993363
  • [Show abstract] [Hide abstract]
    ABSTRACT: Count data with excess zeros are widely encountered in the fields of biomedical, medical, public health and social survey, etc. Zero-inflated Poisson (ZIP) regression models with mixed effects are useful tools for analyzing such data, in which covariates are usually incorporated in the model to explain inter-subject variation and normal distribution is assumed for both random effects and random errors. However, in many practical applications, such assumptions may be violated as the data often exhibit skewness and some covariates may be measured with measurement errors. In this paper, we deal with these issues simultaneously by developing a Bayesian joint hierarchical modeling approach. Specifically, by treating intercepts and slopes in logistic and Poisson regression as random, a flexible two-level ZIP regression model is proposed, where a covariate process with measurement errors is established and a skew-t-distribution is considered for both random errors and random effects. Under the Bayesian framework, model selection is carried out using deviance information criterion (DIC) and a goodness-of-fit statistics is also developed for assessing the plausibility of the posited model. The main advantage of our method is that it allows for more robustness and correctness for investigating heterogeneity from different levels, while accommodating the skewness and measurement errors simultaneously. An application to Shanghai Youth Fitness Survey is used as an illustrate example. Through this real example, it is showed that our approach is of interest and usefulness for applications.
    Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.980941
  • Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.989466
  • Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.989465
  • Journal of Applied Statistics 04/2015; 42(4). DOI:10.1080/02664763.2014.991072
  • [Show abstract] [Hide abstract]
    ABSTRACT: The multiple non-symmetric correspondence analysis (MNSCA) is a useful technique for analysing the prediction of a categorical variable through two or more predictor variables placed in a contingency table. In MNSCA framework, for summarizing the predictability between criterion and predictor variables, the Multiple-TAU index has been proposed. But it cannot be used to test association, and for overcoming this limitation, a relationship with C-Statistic has been recommended. Multiple-TAU index is an overall measure of association that contains both main effects and interaction terms. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition. On the other hand, the interaction effect represents the combined effect of predictor variables on the response variable. In this paper, we propose a decomposition of the Multiple-TAU index in main effects and interaction terms. In order to show this decomposition, we consider an empirical case in which the relationship between the demographic characteristics of the American people, such as race, gender and location (column variables), and their propensity to move (row variable) to a new town to find a job is considered.
    Journal of Applied Statistics 03/2015;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The multiple non-symmetric correspondence analysis (MNSCA) is a useful technique for analysing the prediction of a categorical variable through two or more predictor variables placed in a contingency table. In MNSCA framework, for summarizing the predictability between criterion and predictor variables, the Multiple-TAU index has been proposed. But it cannot be used to test association, and for overcoming this limitation, a relationship with C-Statistic has been recommended. Multiple-TAU index is an overall measure of association that contains both main effects and interaction terms. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition. On the other hand, the interaction effect represents the combined effect of predictor variables on the response variable. In this paper, we propose a decomposition of the Multiple-TAU index in main effects and interaction terms. In order to show this decomposition, we consider an empirical case in which the relationship between the demographic characteristics of the American people, such as race, gender and location (column variables), and their propensity to move (row variable) to a new town to find a job is considered.
    Journal of Applied Statistics 03/2015; DOI:10.1080/02664763.2015.1023269
  • [Show abstract] [Hide abstract]
    ABSTRACT: In most economic and business surveys, the target variables (e.g. turnover of enterprises, income of households, etc.) commonly resemble skewed distributions with many small and few large units. In such surveys, if a stratified sampling technique is used as a method of sampling and estimation, the convenient way of stratification such as the use of demographical variables (e.g. gender, socioeconomic class, geographical region, religion, ethnicity, etc.) or other natural criteria, which is widely practiced in economic surveys, may fail to form homogeneous strata and is not much useful in order to increase the precision of the estimates of variables of interest. In this paper, a stratified sampling design for economic surveys based on auxiliary information has been developed, which can be used for constructing optimum stratification and determining optimum sample allocation to maximize the precision in estimate.
    Journal of Applied Statistics 03/2015; DOI:10.1080/02664763.2015.1018674