szemszögéből közelíti meg a tárgykört. Bár vannak más, közgazdaságtannal (különösen klasszikus gazdaságelmélettel és -történettel) foglalkozó művek is, a SAGE-enciklopédia napjaink világával, mai kérdésekkel és a társa- dalommal foglalkozik. Ez a sokszerzős, négy- kötetes, mintegy 1000–5000 szavas, 800 jegy- zett cikket tartalmazó tudományos referencia- munka átfogó forrás olyan társadalomtudomá- nyi programokban részt vevő diákok és kutatók számára, akik mai „szemüvegen keresztül”
szeretnék jobban megérteni a közgazdaságtant.
DENIS, D. J. [2016]: Applied Univariate, Bivariate, and Multivariate Statistics. (Egy-, két- és többváltozós alkalmazott statisztika.) Wiley. Hoboken.
A témába átfogó és könnyen érthető beve- zetést nyújtó kötet a társadalom- és a viselke- déstudományok terén alkalmazott statisztikai modellezési technikákról ad áttekintést. A statisztikaelméletet és -módszertant ötvözve
tanulmányozza a helyes adatelemzés technikai és elméleti szempontjait.
Különböző szinteken használt források bemutatásával olyan statisztikai módszereket tárgyal, mint a t-próbák, a korrelációszámítás, illetve egyéb bonyolultabb eljárások (MANOVA, faktoranalízis és strukturális egyenletekkel történő modellezés). Az egyes tudományágakban alkalmazott statisztikai technikák mélyreható magyarázatára, néhány, a képletek és egyenletek alapját képező szak- mai érvet is ismertet. Mindezeken túl felvonul- tat R- és SPSS-szoftvercsomagokra épülő statisztikai módszereket; feltételezett és valós adatokon alapuló példákat, illetve ezekre épülő statisztikai elemzéseket is. Történelmi és filozófiai betekintést nyújt számos, a modern társadalomtudományokban alkalmazott tech- nikába. A kötet honlapján további részletek, adatállományok, egyes gyakorlatokhoz megol- dások, valamint többféle programozási lehető- ség is található.
Társfolyóiratok
A NEMZETKÖZI STATISZTIKAI INTÉZET FOLYÓIRATA
2015. ÉVI 2. SZÁM
Jones, M. C.: On families of distributions with shape parameters.
Boos, D. D. – Osborne, J. A.: Assessing variability of complex descriptive statistics in Monte Carlo studies using resampling methods.
Duarte, B. P. M. – Wong, W. K.: Finding Bayesian optimal designs for nonlinear models:
A semidefinite programming-based approach.
Gomes, M. I. – Guillou, A.: Extreme value theory and statistics of univariate extremes: A review.
Reid, N. – Cox, D. R.: On some principles of statistical inference.
Xu, J. – Kuk, A.: On pooling of data and its relative efficiency.
2015. ÉVI 3. SZÁM
Cox, D. R.: A conversation with John C.
Gower.
Gower, J. C.: The development of statisti- cal computing at Rothamsted.
Fisher, N. I. – van Zwet, W. R.: An inter- view with Jae C. Lee.
Jasra, A.: Approximate Bayesian compu- tation for a class of time series models.
Tam, S.-M. – Clarke, F.: Big data, official statistics and some initiatives by the Australian Bureau of Statistics.
Andridge, R. – Thompson, K. J.: Using the fraction of missing information to identify auxiliary variables for imputation procedures via proxy pattern-mixture models.
Zhou, J. et al.: Coarsened propensity scores and hybrid estimators for missing data and causal inference.
Liu, Z. A. et al.: A conditional approach to measure mortality reductions due to cancer screening.
AZ ANGOL KIRÁLYI STATISZTIKAI TÁRSASÁG FOLYÓIRATA
(A SOROZAT) 2015. ÉVI 4. SZÁM
Shlomo, N. – Goldstein, H.: Editorial: Big data in social research.
Diggle, P. J.: Statistics: A data science for the 21st century.
Hancock, R. et al.: Do household surveys give a coherent view of disability benefit targeting? A multisurvey latent variable analy- sis for the older population in Great Britain.
Carriero, A. – Clark, T. E. – Marcellino, M.:
Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility.
Gottard, A. – Mattei, A. – Vignoli, D.: The relationship between education and fertility in the presence of a time varying frailty compo- nent.
Böheim, R. – Lackner, M.: Gender and risk taking: Evidence from jumping competi- tions.
Katki, H. A. et al.: A joint model of persis- tent human papilloma virus infection and
cervical cancer risk: Implications for cervical cancer screening.
Ghilagaber, G. – Wänström, L.: Adjusting for selection bias in assessing the relationship between sibship size and cognitive perfor- mance.
Klausch, T. – Hox, J. – Schouten, B.: Se- lection error in single- and mixed mode sur- veys of the Dutch general population.
Schneider, M. J. – Abowd, J. M.: A new method for protecting interrelated time series with Bayesian prior distributions and synthetic data.
Wheldon, M. C. et al.: Bayesian recon- struction of two-sex populations by age: Esti- mating sex ratios at birth and sex ratios of mortality.
Ross, M. – Wakefield, J.: Bayesian hierar- chical models for smoothing in two-phase stud- ies, with application to small area estimation.
Bartolucci, F. – Dardanoni, V. – Peracchi, F.: Ranking scientific journals via latent class models for polytomous item response data.
Militino, A. F. – Ugarte, M. D. – Goicoa, T.: Deriving small area estimates from infor- mation technology business surveys.
Hund, L. et al.: A Bayesian framework for estimating disease risk due to exposure to uranium mine and mill waste on the Navajo Nation.
2016. ÉVI 1. SZÁM
Varin, C. – Cattelan, M. – Firth, D.: Sta- tistical modelling of citation exchange be- tween statistics journals.
Chaudhuri, K. – Kim, M. – Shin, Y.: Fore- casting distributions of inflation rates: The functional auto-regressive approach.
Fabrizi, E. – Montanari, G. E. – Ranalli, M. G.: A hierarchical latent class model for predicting disability small area counts from survey data.
Tzioumis, K.: Detecting discrimination: A dynamic perspective.
Assaf, S. et al.: Analysing behavioural risk factor surveillance data by using spatially and temporally varying coefficient models.
Sriram, K. – Shi, P. – Ghosh, P.: A Bayes- ian quantile regression model for insurance company costs data.
Feddersen, J. – Metcalfe, R. – Wooden, M.:
Subjective wellbeing: Why weather matters.
van den Brakel, J. A. – Buelens, B. – Boonstra, H.-J.: Small area estimation to quantify discontinuities in repeated sample surveys.
Berger, Y. G. – Priam, R.: A simple vari- ance estimator of change for rotating repeated surveys: An application to the European Union statistics on income and living conditions household surveys.
Marie, O.: Police and thieves in the stadi- um: Measuring the (multiple) effects of foot- ball matches on crime.
Berchuck, S. I. et al.: Spatially modelling the association between access to recreational facilities and exercise: The “multi-ethnic study of atherosclerosis”.
AZ AMERIKAI STATISZTIKAI TÁRSASÁG FOLYÓIRATA
2015. ÉVI 510. SZÁM
Scott, J. G. et al.: False discovery rate re- gression: An application to neural synchrony detection in primary visual cortex.
Kong, X. – Wang, M.-C. – Gray, R.: Anal- ysis of longitudinal multivariate outcome data from couples cohort studies: Application to HPV transmission dynamics.
Li, J. et al.: Survival analysis of loblolly pine trees with spatially correlated random effects.
Xu, Y. et al.: MAD Bayes for tumor heter- ogeneity – Feature allocation with exponential family sampling.
Pimentel, S. D. et al.: Large, sparse opti- mal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons.
Yao, H. et al.: Bayesian inference for multivariate meta-regression with a partially observed within-study sample covariance matrix.
Hadjipantelis, P. Z. et al.: Unifying ampli- tude and phase analysis: A compositional data approach to functional multivariate mixed- effects modeling of Mandarin Chinese.
Tao, R. et al.: Analysis of sequence data under multivariate trait-dependent sampling.
Barney, B. J. et al.: Joint Bayesian model- ing of binomial and rank data for primate cognition.
Zhao, Y.-Q. et al.: New statistical learning methods for estimating optimal dynamic treatment regimes.
Cook, R. D. – Zhang, X.: Foundations for envelope models and methods.
Wu, C. F. J.: Post-Fisherian experimenta- tion: From physical to virtual.
Chiou, S. H. – Kang, S. – Yan, J.: Semipar- ametric accelerated failure time modeling for clustered failure times from stratified sampling.
Cui, H. – Li, R. – Zhong, W.: Model-free feature screening for ultrahigh dimensional discriminant analysis.
Jiang, B. – Ye, Ch. – Liu, J. S.: Nonpara- metric k-sample tests via dynamic slicing.
Preuss, Ph. – Puchstein, R. – Dette, H.:
Detection of multiple structural breaks in multivariate time series.
Ma, W. – Hu, F. – Zhang, L.: Testing hy- potheses of covariate-adaptive randomized clinical trials.
Yi, G. Y. et al.: Functional and structural methods with mixed measurement error and misclassification in covariates.
Vallejos, C. A. – Steel, M. F. J.: Objective Bayesian survival analysis using shape mix- tures of log-normal distributions.
Lee, J. et al.: Bayesian dose-finding in two treatment cycles based on the joint utility of efficacy and toxicity.
Chen, X. – Wan, A. T. K. – Zhou, Y.: Effi- cient quantile regression analysis with missing observations.
Sgouropoulos, N. – Yao, Q. – Yastremiz, C.: Matching a distribution by matching quan- tiles estimation.
Lai, R. C. S. – Hannig, J. – Lee, T. C. M.:
Generalized fiducial inference for ultrahigh- dimensional regression.
Wen, K. – Wu, X.: An improved transfor- mation-based kernel estimator of densities on the unit interval.
Zhu, K. – Ling, S.: LADE-based inference for ARMA models with unspecified and heavy-tailed heteroscedastic noises.
Ma, L.: Scalable Bayesian model averag- ing through local information propagation.
Crane, H.: Clustering from categorical da- ta sequences.
Radchenko, P. – Qiao, X. – James, G. M.:
Index models for sparsely sampled functional data.
Gregory, K. B. et al.: A two-sample test for equality of means in high dimension.
Chen, Y. et al.: Statistical analysis of Q- matrix based diagnostic classification models.
2015. ÉVI 511. SZÁM
Finucane, M. M. et al.: Semiparametric Bayesian density estimation with disparate data sources: A meta-analysis of global child- hood undernutrition.
Hwang, B. S. – Chen, Z.: An integrated Bayesian nonparametric approach for stochastic
and variability orders in ROC curve estimation:
An application to endometriosis diagnosis.
Zubizarreta, J. R.: Stable weights that balance covariates for estimation with incom- plete outcome data.
Hokayem, Ch. – Bollinger, Ch. – Ziliak, J.
P.: The role of CPS nonresponse in the meas- urement of poverty.
Huang, Ch. – Styner, M. – Zhu, H.: Clus- tering high-dimensional landmark-based two- dimensional shape data.
Hu, Y.-J. et al.: Proper use of allele- specific expression improves statistical power for cis-eQTL mapping with RNA-seq data.
Sun, W. et al.: IsoDOT detects differential RNA-isoform expression/usage with respect to a categorical or continuous covariate with high sensitivity and specificity.
Kim, H. J. et al.: Simultaneous edit- imputation for continuous microdata.
Chien, L.-Ch. et al.: Smoothed Lexis dia- grams with applications to lung and breast cancer trends in Taiwan.
Imai, K. – Ratkovic, M.: Robust estimation of inverse probability weights for marginal structural models.
Vermeulen, K. – Vansteelandt, S.: Bias- reduced doubly robust estimation.
Volfovsky, A. – Hoff, P. D.: Testing for nodal dependence in relational data matrices.
Fosdick, B. K. – Hoff, P. D.: Testing and modeling dependencies between a network and nodal attributes.
Azzimonti, L. et al.: Blood flow velocity field estimation via spatial regression with PDE penalization.
Villa, C. – Walker, S. G.: An objective ap- proach to prior mass functions for discrete parameter spaces.
Wang, T. – Xia, Y.: Whittle likelihood es- timation of nonlinear autoregressive models with moving average residuals.
Boente, G. – Salibian-Barrera, M.: S-estima- tors for functional principal component analysis.
Zhu, J. – Raghunathan, T. E.: Conver- gence properties of a sequential regression multiple imputation algorithm.
Chen, H. Y. – Rader, D. E. – Li, M.: Like- lihood inferences on semiparametric odds ratio model.
Jiang, J. – Nguyen, T. – Rao, J. S.: The EMS algorithm: Model selection with incom- plete data.
Pan, D. et al.: Regression analysis of addi- tive hazards model with latent variables.
Qiu, Y. – Chen, S. X.: Bandwidth selection for high-dimensional covariance matrix esti- mation.
Yau, Ch. Y. – Tang, Ch. M. – Lee, T. C.
M.: Estimation of multiple-regime threshold autoregressive models with structural breaks.
Cao, H. et al.: Analysis of the proportional hazards model with sparse longitudinal covari- ates.
Kirch, C. – Muhsal, B. – Ombao, H.: De- tection of changes in multivariate time series with application to EEG data.
Azriel, D. – Schwartzman, A.: The empiri- cal distribution of a large number of correlated normal variables.
Pedeli, X. – Davison, A. C. – Fokianos, K.:
Likelihood estimation for the INAR(p) model by saddlepoint approximation.
Chang, L.-B. – Geman, D.: Tracking cross- validated estimates of prediction error as studies accumulate.
Liang, F. – Song, Q. – Qiu, P.: An equivalent measure of partial correlation coefficients for high-dimensional Gaussian graphical models.
Chen, K. – Lei, J.: Localized functional principal component analysis.
Neve, J. D. – Thas, O.: A regression framework for rank tests based on the proba- bilistic index model.
McElroy, T. – Monsell, B.: Model estima- tion, prediction, and signal extraction for nonstationary stock and flow time series ob- served at mixed frequencies.
2015. ÉVI 512. SZÁM
Morganstein, D.: 2015 ASA Presidential address – Statistics: Making better decisions.
Angrist, J. D. – Rokkanen, M.: Wanna get away? Regression discontinuity estimation of exam school effects away from the cutoff.
Jiang, B. – Liu, J. S.: Bayesian partition models for identifying expression quantitative trait loci.
Wang, L. – Bouchard-Côté, A. – Doucet, A.: Bayesian phylogenetic inference using a combinatorial sequential Monte Carlo method.
Zhang, J. – Su, L.: Temporal autocorrela- tion-based beamforming with MEG neuroim- aging data.
Rosenbaum, P. R.: Some counterclaims un- dermine themselves in observational studies.
Mohler, G. O. et al.: Randomized con- trolled field trials of predictive policing.
Morgan, K. L. – Rubin, D. B.: Rerandomi- zation to balance tiers of covariates.
McKeague, I. W. – Qian, M.: An adaptive resampling test for detecting the presence of significant predictors.
Lee, S. S. M. – Soleymani, M.: A simple formula for mixing estimators with different convergence rates.
Bhattacharya, A. et al.: Dirichlet–Laplace priors for optimal shrinkage.
Wang, W.: Exact optimal confidence inter- vals for hypergeometric parameters.
Guhaniyogi, R. – Dunson, D. B.: Bayesian compressed regression.
Guo, Z. et al.: Groupwise dimension re- duction via envelope method.
Galfao, A. F. – Wang, L.: Uniformly semi- parametric efficient estimation of treatment effects with a continuous treatment.
Jobe, J. M. – Pokojovy, M.: A cluster-based outlier detection scheme for multivariate data.
Martin, R.: Plausibility functions and exact frequentist inference.
Zhou, J. et al.: Bayesian factorizations of big sparse tensors.
Zhao, J. – Shao, J.: Semiparametric pseu- do-likelihoods in generalized linear models with nonignorable missing data.
Calvet, L. E. – Czellar, V. – Ronchetti, E.:
Robust filtering.
Song, Q. – Liang, F.: High-dimensional variable selection with reciprocal L1- regularization.
Martin, R. – Liu, Ch.: Marginal inferential models: Prior-free probabilistic inference on interest parameters.
Kasahara, H. – Shimotsu, K.: Testing the number of components in normal mixture regression models.
Sewell, D. K. – Chen, Y.: Latent space models for dynamic networks.
Wang, L. – Peng, B. – Li, R.: A high- dimensional nonparametric multivariate test for mean vector.
Wu, Y. – Ma, Y. – Yin, G.: Smoothed and corrected score approach to censored quantile regression with measurement errors.
McCormick, T. H. – Zheng, T.: Latent sur- face models for networks using aggregated relational data.
Xu, J. – Chen, J. – Qian, P. Z. G.: Sequen- tially refined latin hypercube designs: Reusing every point.
Simon, N. – Tibshirani, R.: A permutation approach to testing interactions for binary response by comparing correlations between classes.
Jiang, W. – Zhao, Y.: On asymptotic dis- tributions and confidence intervals for LIFT measures in data mining.
Wang, X. et al.: Conditional distance cor- relation.
Datta, G. S. – Mandal, A.: Small area es- timation with uncertain random effects.
Frandsen, B. R.: Treatment effects with censoring and endogeneity.
Calonico, S. – Cattaneo, M. D. – Titiunik, R.: Optimal data-driven regression discontinui- ty plots.
Zhu, R. – Zeng, D. – Kosorok, M. R.: Rein- forcement learning trees.
Delaigle, A. – Zhou, W.-X.: Nonparamet- ric and parametric estimators of prevalence from group testing data with aggregated covariates.
Shao, X.: Self-normalization for time se- ries: A review of recent developments.
NEMZETKÖZI ELMÉLETI ÉS ALKALMAZOTT STATISZTIKAI FOLYÓIRAT
2014. ÉVI 1. SZÁM
Fried, R. – Kuhnt, S. – Müller, C. H.: Pref- ace.
Denecke, L. – Müller, C. H.: Consistency of the likelihood depth estimator for the corre- lation coefficient.
Hubert, M. – Rousseeuw, P. – Vakili, K.:
Shape bias of robust covariance estimators: An empirical study.
Filzmoser, P. – Ruiz-Gazen, A. – Thomas- Agnan, C.: Identification of local multivariate outliers.
Lange, T. – Mosler, K. – Mozharovskyi, P.:
Fast nonparametric classification based on data depth.
Nevalainen, J. – Datta, S. – Oja, H.: Infer- ence on the marginal distribution of clustered data with informative cluster size.
Ruckdeschel, P. – Spangl, B. – Pu- pashenko, D.: Robust Kalman tracking and smoothing with propagating and non- propagating outliers.
Kustosz, C. P. – Müller, C. H.: Analysis of crack growth with robust, distribution-free
estimators and tests for non-stationary auto- regressive processes.
Nordhausen, K.: On robustifying some second order blind source separation methods for nonstationary time series.
Zähle, H.: Qualitative robustness of von Mises statistics based on strongly mixing data.
Aquaro, M. – Čížek, P.: Robust estimation of dynamic fixed-effects panel data models.
Neykov, N. M. – Filzmoser, P. – Neytchev, P. N.: Ultrahigh dimensional variable selec- tion through the penalized maximum trimmed likelihood estimator.
Shevlyakova, M. – Morgenthaler. S.:
Sliced inverse regression for survival data.
Bednarski, T.: On robust causality nonre- sponse testing in duration studies under the Cox model.
Toman, A.: Robust confirmatory factor analysis based on the forward search algo- rithm.
2014. ÉVI 2. SZÁM
Singh, S. – Jain, K. – Sharma, S.: Repli- cated measurement error model under exact linear restrictions.
Veres-Ferrer, E. J. – Pavía, J. M.: On the relationship between the reversed hazard rate and elasticity.
Zhang, Q. – Yang, W. – Hu, S.: On Baha- dur representation for sample quantiles under
-mixing
α sequence.
Chen, Z. – Ng, H. K. T. – Nadarajah, S.:
A note on Cochran test for homogeneity in one-way ANOVA and meta-analysis.
Ateya, S. F.: Maximum likelihood estima- tion under a finite mixture of generalized expo- nential distributions based on censored data.
Xu, D. – Zhang, Z. – Wu, L.: Variable se- lection in high-dimensional double generalized linear models.
Ciuperca, G.: Model selection by LASSO methods in a change-point model.
Song, G. J. – Wang, Q. W.: On the weighted least-squares, the ordinary least- squares and the best linear unbiased estimators under a restricted growth curve model.
Şiray, G. Ü. – Kaçiranlar, S. – Sakallioğlur, S.: r – k class estimator in the linear regression model with correlated errors.
Salehi, M. – Jamalizadeh, A. – Doost- parast, M.: A generalized skew two-piece skew-elliptical distribution.
Giménez, P. – Patat, M. L.: Local influ- ence for functional comparative calibration models with replicated data.
Czado, C. – Schabenberger, H. – Erhardt, E.: Non nested model selection for spatial count regression models with application to health insurance.
Baltagi, B. H. – Liu, L.: Testing for spatial lag and spatial error dependence using double length artificial regressions.
Reschenhofer, E. – Ploberger, W. – Le- hecka, G. V.: Detecting fuzzy periodic patterns in futures spreads.
Gómez-Déniz, E. – Vázquez-Polo, F. J. – García-García, V.: A discrete version of the half-normal distribution and its generalization with applications.
Peiris, S.: Testing the null hypothesis of zero serial correlation in short panel time series: A comparison of tail probabilities.
Schepsmeier, U. – Stöber, J.: Derivatives and Fisher information of bivariate copulas.
Haslett, S. J. et al.: Equalities between OLSE, BLUE and BLUP in the linear model.
Li, W.: Local expectations of the popula- tion spectral distribution of a high-dimensional covariance matrix.
2014. ÉVI 3. SZÁM
Maya, R. et al.: Estimation of the Renyi’s residual entropy of order α with dependent data.
Rodrigues, P. C. et al.: Structured orthog- onal families of one and two strata prime basis factorial models.
Ma, T. F. – Liu, S.: Pitman closeness of the class of isotonic estimators for ordered scale parameters of two gamma distributions.
Karlsson, M. – Laitila, T.: Finite mixture modeling of censored regression models.
Cordeiro, G. M. et al.: Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models.
Schmid, T. – Münnich, R. T.: Spatial ro- bust small area estimation.
Zeller, C. B. – Lachos, V. H. – Vilca, F.:
Labrainfluence diagnostics for Grubbs’s model with asymmetric heavy-tailed distributions.
Anis, M. Z.: Tests of non-monotonic sto- chastic aging notions in reliability theory.
Sha, N. – Pan, R.: Bayesian analysis for step-stress accelerated life testing using Weibull proportional hazard model.
Antoniadis, A. – Gijbels, I. – Lambert- Lacroix, S.: Penalized estimation in additive varying coefficient models using grouped regularization.
Nadar, M. – Fatih Kizilaslan, F.: Classical and Bayesian estimation of P(X < Y) using upper record values from Kumaraswamy’s distribution.
Genç, A. I.: Distribution of product and quotient of bivariate generalized exponential distribution.
Argiento, R. – Guglielmi, A. – Pievatolo, A.: Estimation, prediction and interpretation of NGG random effects models: An applica- tion to Kevlar fibre failure times.
Zheng, J. – Shen, J. – He, S.: Adjusted em- pirical likelihood for right censored lifetime data.
Eryilmaz, S. – Bayramoglu, K.: Life be- havior of δ-shock models for uniformly dis- tributed interarrival times.
Matsuura, S. – Kurata, H.: Principal points for an allometric extension model.
Depraetere, N. – Vandebroek, M.: Order selection in finite mixtures of linear regres- sions.
2014. ÉVI 4. SZÁM
Schnurr, A.: An ordinal pattern approach to detect and to model leverage effects and dependence structures between financial time series.
Faraz, A. et al.: The variable parameters T2 chart with run rules.
Withers, C. S. – Nadarajah, S.: A unified method for constructing expectation tolerance intervals.
Olmos, N. M.: An extension of the general- ized half-normal distribution.
Fernández-Durán, J. J. – Gregorio-Do- mínguez, M. M.: Distributions for spherical data based on nonnegative trigonometric sums.
Beyaztas, U. – Alin, A.: Sufficient jack- knife-after-bootstrap method for detection of influential observations in linear regression models.
Chahkandi, M. – Ahmadi, J. – Baratpour, S.: Non-parametric prediction intervals for the lifetime of coherent systems.
Li, D. – Klesov, O. – Stoica, G.: On the central limit theorem along subsequences of sums of i.i.d. random variables.
Čiginas, A.: On the asymptotic normality of finite population l-statistics.
Hobza, T. – Morales, D. – Pardo, L.: Di- vergence-based tests of homogeneity for spa- tial data.
Dong, Y. – Lee, S. M. S.: Depth functions as measures of representativeness.
Genest, C. – Nešlehová, J. G.: On tests of radial symmetry for bivariate copulas.
Alvarez-Andrade, S. – Bouzebda, S.: As- ymptotic results for hybrids of empirical and partial sums processes.
Wang, L. – Wang, J.: Wavelet estimation of the memory parameter for long range de- pendent random fields.
Al-Kandari, N. M. – Aly, E.-E. A. A.: An ANOVA-type test for multiple change points.
Kamalja, K. K.: On the joint distribution of success runs of several lengths in the sequence of MBT and its applications.
Kawano, S.: Selection of tuning parame- ters in bridge regression models via Bayesian information criterion.
2015. ÉVI 1. SZÁM
Rakitzis, A. C. – Antzoulakos, D. L.: Start- up demonstration tests with three-level classi- fication.
Xia, N. – Bai, Z.: Functional CLT of eigen- vectors for large sample covariance matrices.
Hesamian, G. – Chachi, J.: Two-sample Kolmogorov–Smirnov fuzzy test for fuzzy random variables.
Solanki, R. S. – Singh, H. P.: Efficient classes of estimators in stratified random sampling.
Mandal, N. K. et al.: Optimum mixture de- signs in a restricted region.
Balakrishnan, N. – Koukouvinos, C. – Parpoula, C.: Analyzing supersaturated de- signs for discrete responses via generalized linear models.
Inan, D.: Combining the Liu-type estima- tor and the principal component regression estimator.
Li, S. et al.: The estimation and inference on the equal ratios of means to standard devia- tions of normal populations.
Groenitz, H.: Using prior information in privacy-protecting survey designs for categori- cal sensitive variables.
Zheng, F. – Zang, Q.: A general pattern of asymptotic behavior of the R/S statistics for linear processes.
Parvardeh, A.: A note on the asymptotic distribution of the estimation of the mean past lifetime.
Huang, J. – Yang, H.: On a principal com- ponent two-parameter estimator in linear model with autocorrelated errors.
Arashi, M. – Valizadeh, T.: Performance of Kibria’s methods in partial linear ridge regres- sion model.
Park, S. – Lim, J.: On censored cumulative residual Kullback–Leibler information and goodness-of-fit test with type II censored data.
Jiang, H. – Dong, X.: Parameter estimation for the non-stationary Ornstein–Uhlenbeck process with linear drift.
2015. ÉVI 2. SZÁM
Casero-Alonso, V. – López-Fidalgo, J.:
Experimental designs in triangular simultane- ous equations models.
Gündüz, S. – Genç, A. I.: The distribution of the quotient of two triangularly distributed random variables.
Hanna, H. – Tinsson W.: A new class of designs for mixture-of-mixture experiments.
Gunasekera, S.: Generalized inferences of R = Pr(X > Y) for Pareto distribution.
Arslan, O.: Variance-mean mixture of the multivariate skew normal distribution.
Akio Namba, A.: MSE dominance of the positive-part shrinkage estimator when each individual regression coefficient is estimated.
Fotouhi, H. – Golalizadeh, M.: Highly re- sistant gradient descent algorithm for compu- ting intrinsic mean shape on similarity shape spaces.
Zhao, W.et al.: Empirical likelihood based modal regression.
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MATEMATIKAI STATISZTIKAI INTÉZETÉNEK FOLYÓIRATA
2015. ÉVI 3. SZÁM
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Carlin, B. P. – Herring, A. H.: A conversa- tion with Alan Gelfand.
Atkinson, A. C. – Bogacka, B.: A conversa- tion with Professor Tadeusz Caliński.
2015. ÉVI 4. SZÁM
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Puccetti, G. – Wang, R.: Extremal depend- ence concepts.
Chacón, J. E.: A population background for nonparametric density-based clustering.
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Polson, N. G. – Scott, J. G. – Willard, B.
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