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 Showing 1 - 98 of 98 Journals sorted alphabetically Advances in Statistics       (Followers: 9) Afrika Statistika       (Followers: 1) American Journal of Applied Mathematics and Statistics       (Followers: 11) American Journal of Mathematics and Statistics       (Followers: 9) Annals of Data Science       (Followers: 14) Annual Review of Statistics and Its Application       (Followers: 7) Applied Medical Informatics       (Followers: 11) Asian Journal of Mathematics & Statistics       (Followers: 8) Asian Journal of Probability and Statistics Austrian Journal of Statistics       (Followers: 4) Biostatistics & Epidemiology       (Followers: 4) Cadernos do IME : Série Estatística Calcutta Statistical Association Bulletin Communications in Mathematics and Statistics       (Followers: 4) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics: Case Studies, Data Analysis and Applications Comunicaciones en Estadística Econometrics and Statistics       (Followers: 1) Forecasting       (Followers: 1) Foundations and Trends® in Optimization       (Followers: 3) Frontiers in Applied Mathematics and Statistics       (Followers: 1) Game Theory       (Followers: 2) Geoinformatics & Geostatistics       (Followers: 14) Geomatics, Natural Hazards and Risk       (Followers: 13) Indonesian Journal of Applied Statistics International Game Theory Review       (Followers: 1) International Journal of Advanced Statistics and IT&C for Economics and Life Sciences International Journal of Advanced Statistics and Probability       (Followers: 7) International Journal of Algebra and Statistics       (Followers: 3) International Journal of Applied Mathematics and Statistics       (Followers: 3) International Journal of Ecological Economics and Statistics       (Followers: 4) International Journal of Energy and Statistics       (Followers: 3) International Journal of Game Theory       (Followers: 3) International Journal of Mathematics and Statistics       (Followers: 2) International Journal of Multivariate Data Analysis International Journal of Probability and Statistics       (Followers: 4) International Journal of Statistics & Economics       (Followers: 6) International Journal of Statistics and Applications       (Followers: 2) International Journal of Statistics and Probability       (Followers: 3) International Journal of Statistics in Medical Research       (Followers: 5) International Journal of Testing       (Followers: 1) Iraqi Journal of Statistical Sciences Japanese Journal of Statistics and Data Science Journal of Biometrics & Biostatistics       (Followers: 4) Journal of Cost Analysis and Parametrics       (Followers: 5) Journal of Environmental Statistics       (Followers: 4) Journal of Game Theory       (Followers: 1) Journal of Mathematical Economics and Finance Journal of Mathematics and Statistics Studies Journal of Modern Applied Statistical Methods       (Followers: 1) Journal of Official Statistics       (Followers: 2) Journal of Quantitative Economics Journal of Social and Economic Statistics Journal of Statistical Theory and Practice       (Followers: 2) Journal of Statistics and Data Science Education       (Followers: 2) Journal of Survey Statistics and Methodology       (Followers: 4) Journal of the Indian Society for Probability and Statistics Jurnal Biometrika dan Kependudukan Jurnal Ekonomi Kuantitatif Terapan Jurnal Sains Matematika dan Statistika Lietuvos Statistikos Darbai Mathematics and Statistics       (Followers: 2) Methods, Data, Analyses       (Followers: 1) METRON       (Followers: 1) Nepalese Journal of Statistics North American Actuarial Journal       (Followers: 1) Open Journal of Statistics       (Followers: 3) Open Mathematics, Statistics and Probability Journal Pakistan Journal of Statistics and Operation Research       (Followers: 1) Physica A: Statistical Mechanics and its Applications       (Followers: 6) Probability, Uncertainty and Quantitative Risk       (Followers: 2) Ratio Mathematica Research & Reviews : Journal of Statistics       (Followers: 3) Revista Brasileira de Biometria Revista Colombiana de Estadística RMS : Research in Mathematics & Statistics Romanian Statistical Review Sankhya B - Applied and Interdisciplinary Statistics SIAM Journal on Mathematics of Data Science       (Followers: 1) SIAM/ASA Journal on Uncertainty Quantification       (Followers: 2) Spatial Statistics       (Followers: 2) Sri Lankan Journal of Applied Statistics Stat       (Followers: 1) Stata Journal       (Followers: 8) Statistica       (Followers: 6) Statistical Analysis and Data Mining       (Followers: 23) Statistical Theory and Related Fields Statistics and Public Policy       (Followers: 4) Statistics in Transition New Series : An International Journal of the Polish Statistical Association Statistics Research Letters       (Followers: 1) Statistics, Optimization & Information Computing       (Followers: 3) Stats Synthesis Lectures on Mathematics and Statistics       (Followers: 1) Theory of Probability and its Applications       (Followers: 2) Theory of Probability and Mathematical Statistics       (Followers: 2) Turkish Journal of Forecasting       (Followers: 1) VARIANSI : Journal of Statistics and Its application on Teaching and Research Zeitschrift für die gesamte Versicherungswissenschaft

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 Journal of the Indian Society for Probability and StatisticsNumber of Followers: 0      Subscription journal ISSN (Online) 2364-9569 Published by Springer Publishing Company  [24 journals]
• Securing Density Estimates via Smooth Moment-Based Empirical Distribution
Function Approximants

Abstract: Abstract This paper proposes an adaptive density estimation procedure that hinges on securing moment-based approximants of certain splines passing through particular points that are obtained from an appropriately adjusted and truncated empirical distribution function. More specifically, a four-parameter beta density estimate is initially fitted to the data in order to determine the endpoints of the distribution which are combined to the data points. Interpolants of the continuity-corrected empirical distribution function evaluated at these points are then approximated by smooth functions involving polynomials. As a matter of course, the density estimates are obtained by differentiation. Any quantile of the corresponding distribution can thereby be directly evaluated from the associated distribution functions. The Cramér-von Mises goodness-of-fit statistic is utilized as a measure of accuracy. Three illustrative examples are presented.
PubDate: 2022-04-28

• Memory of Distributions: A Renewal Theoretic Approach

Abstract: Abstract Here we propose a new definition of memory of distributions in a renewal-theoretic framework and study the corresponding notions of ageing and ordering of distributions. Discrete life times are also considered.
PubDate: 2022-04-08

• Modeling Structural Breaks in Disturbances Precision or Autoregressive
Parameter in Dynamic Model: A Bayesian Approach

Abstract: Abstract The focus of this paper is the examination of dynamic models in the presence of structural changes either due to disturbances precision or autoregressive parameter under the Bayesian framework. The Bayesian analysis of the dynamic model has been carried out under the mixture of prior distributions for the parameters. The posterior distribution of parameters is derived to obtain Bayes estimators under quadratic loss function ignoring the possibility of structural breaks in regression coefficients. The posterior odds ratio has been developed under the assumption that disturbance precision leads to structural change as against the autoregressive parameter. The theoretical framework is also empirically tested employing data set of Indian companies considering financial variables like debt, profitability, investment etc.; covering the global financial crisis (GFC) period. The empirical exercise carried out highlights 2008–09 as the major structural breakpoint when many Indian companies suffered losses.
PubDate: 2022-04-01

• An R-Estimator in the Errors in Variables Linear Regression Model

Abstract: Abstract This note develops an R estimator of the regression parameters in the errors in variables linear regression model, when the distributions of the vectors of covariates and measurement errors are known. The paper also contains the proof of the asymptotic uniform linearity of a sequence of a simple linear rank statistics based on the residuals of a class of nonlinear parametric regression models where covariates and regression errors are possibly dependent. This result in turn facilitates the proof of the asymptotic normality of the above mentioned R estimator in the errors in variables linear regression model. The Pitman’s asymptotic relative efficiency of this R estimator relative to the bias corrected least squares estimator is shown to increase to infinity as the measurement error variance increases to infinity at some Gaussian errors and covariate distributions.
PubDate: 2022-03-29

• Rectangular Confidence Regions and Prediction Regions in Multivariate
Calibration

Abstract: Abstract The multivariate calibration problem deals with inference concerning an unknown value of a covariate vector based on an observation on a response vector. Two distinct scenarios are considered in the multivariate calibration problem: controlled calibration where the covariates are non-stochastic, and random calibration where the covariates are random. Under controlled calibration, a problem of interest is the computation of a confidence region for the unknown covariate vector. Under random calibration, the problem is that of computing a prediction region for the covariate vector. Assuming the standard multivariate normal linear regression model, rectangular confidence and prediction regions are derived using a parametric bootstrap approach. Numerical results show that the regions accurately maintain the coverage probabilities. The results are illustrated using examples. The regions currently available in the literature are all ellipsoidal, and this work is the first attempt to derive rectangular regions.
PubDate: 2022-03-28

• E-Bayesian and Hierarchical Bayesian Estimation of Rayleigh Distribution
Parameter with Type-II Censoring from Imprecise Data

Abstract: Abstract Bayesian estimation methods for Rayleigh distribution parameter affect accurate information. However, in real-world conditions, empirical performance results cannot always be recorded or measured accurately. Thus, we'd like to generalize the estimated methods for real numbers to fuzzy numbers. during this paper, Bayesian, E-Bayesian and Hierarchical Bayesian (H-Bayesian) methods are discussed for Rayleigh distribution parameter on the idea of a Type-II censoring schemes under the squared error loss function. Data is taken into account as imprecise and within the form fuzzy numbers. Then, the efficiency of estimation methods is compared via Monte Carlo simulation. Finally, a true data set for the needs described is analyzed.
PubDate: 2022-01-20

• Some Aging Notions Based on the Laplace Transform of Bivariate Residual
Life

Abstract: Abstract The Laplace transform of the bivariate residual life (LTBR) has some perceptive meaning in reliability and survival analysis. The purpose of this paper is to study new notions of aging based on the LTBR. In this paper, the LTBR and its properties and applications are derived. We also propose a new aging class related to LTBR and called bivariate new better than used in Laplace transform order (BNBUL). The relationships of the new aging class with other existing aging classes are also pointed out.
PubDate: 2022-01-05

• Functional L.I.L. for the Upper Extremes of i.i.d. Random Variarbles from
a Sub-class of Heavy Tailed Distributions

Abstract: Abstract Let $$\left\{ X_n\right\}$$ be a sequence of independent and identically distributed non-negative valued random variables defined over a common probability space and let the common distribution function F be continuous. Suppose that F belongs to the domain of partial attraction of a max-semi Frechet law. Define for any $$r\ge 1$$ and $$n\ge r$$ , $$M_{r,[nt]}$$ = the rth highest among $$\left\{ X_1,X_2,\ldots ,X_{[nt]}\right\}$$ if $$\frac{r}{n} \le t \le 1,$$ and = min $$\left\{ X_1,X_2,\ldots ,X_r\right\}$$ if $$0\le t \le \frac{r}{n}.$$ We establish below, a functional law of the iterated logarithm for the sequence $$(M_{r,[nt]}, 0\le t \le 1)$$ , properly normalized, under the $$M_1$$ -topology.
PubDate: 2021-12-01

• Nonparametric recursive estimate for right-censored conditional mode
function with ergodic functional data

Abstract: Abstract As an extension of some recent works that are essential references for this current contribution, we provide a recursive nonparametric approach to estimate the conditional mode function $$\Theta (x)$$ of the conditional density of a scalar positive random variable Z given a Hilbertian explanatory process $$X = x,$$ denoted by $$\xi ^{x}(z),$$ based on the randomly right-censorship model which is the new and main factor here. Our nonparametric model takes into account the fact that the response variable Z referred as a survival time is right-censored by another variable W independent of (Z, X). Afterwards, we establish, under stationary and ergodic conditions, by using an adaptive exponential inequality to this context, some theoretical properties of the resulting estimator including the uniform almost sure convergence (with rates), after establishing the pointwise ones. Finally, an application based on simulated data is conducted to illustrate our results.
PubDate: 2021-12-01

• Unit Log-Logistic Distribution and Unit Log-Logistic Regression Model

Abstract: Abstract In this paper, the unit log-logistic distribution was proposed. This distribution is obtained through by transformation of a random variable with log-logistic distribution. The unit log-logistic has closed forms for the cumulative distribution function and quantile function. Subsequently, the unit log-logistic regression model, with parametrization in the median was defined. So, in the presence of outliers, this model is more robust than the models with parametrization in the mean. The maximum likelihood method was used to estimate the parameters. The validity of the estimators of this model is shown through Monte Carlo simulations. Application to real data showed that the new model has a better fit than the popular beta regression model.
PubDate: 2021-12-01

• A New Modified Alpha Power Weibull Distribution: Properties, Parameter
Estimation and Application

Abstract: Abstract A new lifetime distribution called alpha power modified Weibull (APMW) distribution based on the alpha power transformation method has been studied. Various statistical properties of the newly developed distribution including quantiles, moments, stress-strength parameter, Bonferroni and Lorentz curve, residual and reversed residual lifetime function, stress-strength parameter, entropy and order statistics have been obtained. Percentage points of the APMW distribution for different values of the parameters have been obtained. The method of maximum likelihood estimation (MLE) has been used for estimating the parameters. A simulation study has been performed to evaluate the behaviour of the MLEs in terms of the sample size n and revealed that as the value of the sample size increases the value of the mean square error decreases showing the reliability of the estimators. The efficiency and flexibility of the new distribution are illustrated by analysing three real-life data sets. In each case, the APMW distribution provides a better fit indicating that the APMW distribution is a justifiable choice for fitting the considered data sets.
PubDate: 2021-12-01

• Estimation of Weighted Residual Inaccuracy Measure for Right Censored
Dependent Data

Abstract: Abstract Recently inaccuracy measure has been widely used as a useful tool for measuring error in experimental results. The present paper we proposes nonparametric estimators for the weighted inaccuracy measure based on right-censored dependent data. The asymptotic properties of the proposed estimators are discussed. We illustrated the performance of the estimator using simulated and a real-life data set.
PubDate: 2021-12-01

• Generalised Skew Log Laplace Distribution

Abstract: Abstract Pareto distribution was formulated to characterise incomes. Generally it is observed that reported incomes are understatements of the true values. Hartley and Revankar (J Econom 2:327–341, 1974) developed a model for underreported income, which was named as Skew log Laplace distribution (SLLD) by Kozubowski and Podgorski (Int Math J 3:467–495, 2003). The main aim of this paper is to develop a distribution of reported income, when reported incomes are mixture of true and underreported incomes. We named this distribution as generalised skew log Laplace distribution (GSLLD) as SLLD is a particular case of GSLLD. The maximum likelihood estimators of the parameters (MLE) of GSLLD when scale parameter is known are obtained and it is proved that MLEs are best asymptotically normal. The confidence intervals of the parameters are provided. The underreported incomes can be analysed using GSLLD or SLLD. The likelihood ratio test is derived to choose between GSLLD and SLLD. Finally, an application of GSLLD to real life income values is presented.
PubDate: 2021-10-15

• Effective Reproduction Number and Dispersion under Contact Tracing and
Lockdown on COVID-19 in Karnataka

Abstract: Abstract We study the effectiveness and limitations of contact-tracing, quarantine, and lockdown measures used in India to control the spread of COVID-19 infections. Using data provided in the media bulletins of Government of Karnataka we observe that the so called $$20-80$$ rule holds for secondary infections and classify them into clusters. Using a mixture of Poisson with Gamma model we establish that clusters show variation in deceased rates ( $$0\%-17.31 \%$$ ), low reproduction numbers ( $$0.21-0.77$$ ), small dispersion( $$0.06-0.18$$ ), and that super-spreading events can occur. Further, migration due to relaxation in lockdown is unlikely to be the sole cause of recent surge. The methodology presented is universal in nature and can be applied whenever such precise data is available.
PubDate: 2021-09-24

• Exponentiated Power Muth Distribution and Associated Inference

Abstract: Abstract In this article we introduce a generalized form of power Muth distribution, so-called exponentiated power Muth distribution with increasing, decreasing, bathtub and upside-down bathtub shaped hazard rates and investigate its important properties including analytical expression for moments based on generalized integro-exponential function, moment generating function, reliability measures and an uncertainty measure extropy. The maximum likelihood estimation method is used to estimate the parameters of the model and their performance is assessed via simulation study. Finally, the supremacy of the model is illustrated compared to its sub models and some other competing models using eight real data sets from diverse fields.
PubDate: 2021-08-04

• Kernel Estimation of Entropy Function for Length-Biased Censored Data

Abstract: Abstract In this article, we propose nonparametric kernel type estimators for the Shannon differential entropy for length-biased censored data. The asymptotic properties of the estimators are established under specific regularity conditions. The performance of the estimators is examined through simulated observations and is compared using mean squared errors for various sample sizes. The applicability of the estimators is demonstrated using real data.
PubDate: 2021-07-17

• Spatiotemporal Analysis of Ridesourcing and Taxi Usage by Zones

Abstract: Abstract The burst of demand for transportation network companies (TNCs) such as Uber, Lyft, or Via, has significantly changed the transportation landscape and dramatically disrupted the Vehicle for Hire market that used to be dominated by taxicabs for many years. Since first being introduced by Uber in 2009, ridesourcing services have rapidly penetrated the market. This paper aims to investigate temporal and spatial patterns in taxi and TNC usage based on data at the taxi zone level in New York City. Similar analysis is possible using rideshare data that is specific to different countries. To our data, we fit suitable time series models to estimate the temporal patterns. Next, we subtract out the temporal effects and investigate spatial dependence in the residuals using global and local Moran’s I statistics. We discuss the relation between the spatial correlations and the demographic and land use effects at the taxi zone level. Estimating and removing these effects via a multiple linear regression model and recomputing the Moran’s I statistics on the resulting residuals enables us to investigate spatial dependence after accounting for these effects. Our analysis indicates interesting patterns in spatial correlations between taxi zones in NYC and over time, indicating that predictive modeling of ridesourcing usage benefits from accommodating both temporal and spatial dependence.
PubDate: 2021-06-01

• On Generating Families of Power Quantile Distributions for Modeling
Waiting and Repair Times Data

Abstract: Abstract In this study, we introduce a new rich family of distributions defined by a simple power quantile function. We discuss some of its interesting properties, presenting all the main distributional functions, expansion of the quantile function, ordinary moments, incomplete moments and L-moments. Then the focus is on some special members, providing their important theoretical features along with the implications to the real life applications. We show the applicability of the proposed family by considering two practical data sets. The estimates of the model parameters are obtained using the percentiles and maximum likelihood methods.
PubDate: 2021-06-01

• A Review of Transmuted Distributions

Abstract: Abstract A comprehensive review of transmuted distributions is provided. Nearly thirty such distributions are reviewed. Real data applications are provided comparing the reviewed distributions to other classes of distributions. This review could serve as an important reference and encourage developments of further transmuted distributions that could model complicated phenomena more accurately.
PubDate: 2021-06-01

• Improved Estimators of the Hazard Rate from a Selected Gamma Population
Under an Asymmetric Loss

Abstract: Abstract We consider the problem of estimation of the hazard rate from a selected gamma population. Let $$\Pi _{1}$$ , $$\Pi _{2}$$ be two populations, where $$\Pi _{i}$$ follows an one parameter gamma distribution with hazard rate $$\lambda _{i}$$ , $$i=1,2$$ . Let $$X_{i1},X_{i2},\ldots ,X_{in}$$ be an independent random sample drawn from the population $$\Pi _{i}$$ , $$i=1,2$$ . Consider $$X_{i}={\sum _{j=1}}^{n}X_{ij}$$ to be the sample mean of the ith population The natural selection rule is to select the population with largest(smallest) mean. That is $$\Pi _{i}$$ if $$X_{i}=max(min)(X_{1},X_{2})$$ . Some natural estimator are proposed and it was shown that they are admissible within a class of estimators with respect to the entropy loss. Further some improved estimators are obtained which improves upon the natural estimators.
PubDate: 2021-06-01

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