Papers by Yogendra Chaubey
Unemployment, Search and the Gender Wage Gap: A Structural Model (C Belzil & X Zhang) Kullbac... more Unemployment, Search and the Gender Wage Gap: A Structural Model (C Belzil & X Zhang) Kullback-Leibler Optimization of Density Estimates (A Berlinet & E Brunel) The Asymptotic Distribution of Spacings of Order Statistics (M G Bickis) Second-Order Moments and Mutual Information in the Analysis of Time Series (D R Brillinger) On the Robustness of Relative Surprise Inferences to the Choice of Prior (M Evans & T Zou) Using Survival Analysis in Pretern Birth Study (C Y Fu & S H Liu) A Nested Frailty Survival Model for Recurrent Events (R Ma et al.) Testing Goodness-of-Fit of the Gamma Models (C E Marchetti et al.) On Frailty Models and Copulas (D Oakes) Surrogate Data and Fractional Brownian Motion (P Rabinovitch) Nonlinear Mixed Effects Models: Recent Developments (P S R S Rao & N Zaino) Computational Sequence Analysis: Genomics and Statistical Controversies (P K Sen) Universal Optimality of Completely Randomized Designs (K R Shah & B K Sinha) Generalized Smoothed Estimating Functions with Censored Observations (A Thavaneswaran & J Singh) and other papers.
WORLD SCIENTIFIC eBooks, 2013
Preface Hypothesis Assessment Using the Bayes Factor and Relative Belief Ratio (Z Baskurt and M E... more Preface Hypothesis Assessment Using the Bayes Factor and Relative Belief Ratio (Z Baskurt and M Evans) An Analysis of 1990-2011 Ontario Surface Air Temperatures (D R Brillinger) A Markov Chain Monte Carlo Sampler for Gene Genealogies Conditional on Haplotype Data (K M Burkett, B McNeney and J Graham) Jackknifing Stochastic Restricted Ridge Estimator with Heteroscedastic Errors (Y P Chaubey, M Khurana and S Chandra) Molecular Classification of Acute Leukemia (G Chen and J Wu) A Note on a Nonparametric Trend Test and Its Competitors (K Deka and B Gogoi) Local Search for Identifying Communities in Large Random Graphs (N Deo and M Vasudevan) Data Mining Approaches to Multivariate Biomarker Discovery (D M Dziuda) On Sequential Change-Point Detection Strategies (E Gombay) Multi-Item Two-Stage EOQ Model Incorporating Quantity and Freight Discounts (D Gupta, K Gandhi, S Makkar and P C Jha) An Improved Bound on Average Codeword Length of Variable Length Error Correcting Codes (R Gupta and B Sharma) Trimmed Analysis of Variance: A Robust Modification of ANOVA (G S Mudholkar, D K Srivastava, C E Marchetti and A G Mudholkar) Bayesian Predictive Inference under Sequential Sampling with Selection Bias (B Nandram and D Kim) Tests For and Against Uniform Stochastic Ordering on Multinomial Parameters Based on Φ-Divergences (J Peng) Development and Management of National Health Plans: Health Economics and Statistical Perspectives (P K Sen) A Unified Approach between Population-Averaged and Cluster-Specific Analyses for Multilevel Zero-Inflated Count Data (G Sneddon, M T Hasan and R Ma) Meta-Analysis of Binary Outcomes Data in Clinical Trials (P Subbaiah, A Mallick and T K Desai) Risk Reduction of the Supply Chain through Pooling Losses in Case of Bankruptcy of Suppliers Using the Black-Scholes-Merton Pricing Model (R Valvarde and M Talla) Random Effects Modelling of Non-Gaussian Time Series Responses with Covariates (G Yan, M T Hasan and R Ma).
Journal of statistical theory and practice, Oct 26, 2018
This paper considers estimation of the density function in the context of biased data using thres... more This paper considers estimation of the density function in the context of biased data using thresholded wavelet estimator. We adapt the method of estimating the square root of the density function as advocated in Pinheiro and Vidakovic (Comput Stat Data Anal 25:399-415, 1997) in the iid case. The density estimator is obtained by squaring the resulting estimator that as a result guarantees non-negativity. It is shown that the resulting estimator achieves the optimal 2 convergence in Besov spaces B s pq (M) for p ≥ 2 whereas for p ∈ [1, 2) there is a logarithmic penalty attached to the optimal order. Finally, a simulation study shows that the resulting estimator may be favored over the usual wavelet estimator, with a proper choice of the preliminary estimator of the biased density.
Calcutta Statistical Association Bulletin, Mar 1, 2009
Extreme value and extreme spacing distributions are elegant and important artifacts of statistica... more Extreme value and extreme spacing distributions are elegant and important artifacts of statistical theory and practice. However, in statistical education, due to the highly technical nature of the subject, they are generally treated as special topics. But, as demonstrated by Freimer et al. (1989), the asymptotic distributions of the extremes and extreme spacings form expressions. Interestingly, it is seen that the extreme value theory for the RRIG population, and not of the IG population, is somewhat analogous to the Gaussian distribution.
Communications in Statistics, Apr 1, 2015
Here, we consider wavelet based estimation of the derivatives of a probability density function u... more Here, we consider wavelet based estimation of the derivatives of a probability density function under random sampling from a weighted distribution and extend the results regarding the asymptotic convergence rates under the i.i.d. setup studied in Prakasa Rao (1996) to the biased-data setup. We compare the performance of the wavelet based estimator with that of the kernel based estimator obtained by differentiating the Efromovich (2004) kernel density estimator through a simulation study.
arXiv (Cornell University), Jan 19, 2016
In this note we provide a simple approximation theory motivation for the circular kernel density ... more In this note we provide a simple approximation theory motivation for the circular kernel density estimation and further explore the usefulness of the wrapped Cauchy kernel in this context. It is seen that the wrapped Cauchy kernel appears as a natural candidate in connection to orthogonal series density estimation on a unit circle. This adds further weight to the considerable role of the wrapped Cauchy in circular statistics.
ABSTRACT This note considers the wavelet based linear density estimator for the probability densi... more ABSTRACT This note considers the wavelet based linear density estimator for the probability density function considered by B.L.S. Prakasa Rao [J. Indian Stat. Assoc. 41, No. 2, 369–379 (2003)]. The results obtained for associated sequences by Prakasa Rao are extended to the case of negatively dependent sequences.
Calcutta Statistical Association Bulletin, Mar 1, 2013
Let {X 1 , ..., X n } be a random sample from a continuous distribution F defined on the k−dimens... more Let {X 1 , ..., X n } be a random sample from a continuous distribution F defined on the k−dimensional Euclidean space R k , for some k ≥ 1. In many statistical applications we are interested in statistical properties of a function h(X 1 , ..., X m) of m ≥ 1 observations. Frees (1994, J. Amer. Stat. Assoc.) considered estimating the density function g associated with the distribution function
Series on quality, reliability and engineering statistics, Jul 1, 1998
... Page 108. 92 YP Chaubey and PK Sen where A'(< oo) does not depend on (s, t). ... more ... Page 108. 92 YP Chaubey and PK Sen where A'(< oo) does not depend on (s, t). Next we give the asymptotic representation for/(<) which would provide the asymptotic normality as well as the rate of convergence of the estimator. ...
Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits, 2016
Sankhya B, 2021
Let {Xn, n ≥ 1} be a sequence of stationary non-negative associated random variables with common ... more Let {Xn, n ≥ 1} be a sequence of stationary non-negative associated random variables with common marginal distribution function F(x) and quantile function Q(u), where Q(u) is defined as F(Q(u)) = u. Here we consider the smooth estimation of Q(u), adapted from generalized kernel smoothing (Cheng and Parzen J. Stat. Plann. Infer. 59, 291–307, 1997) of the empirical quantile function. Some asymptotic properties of the kernel quantile estimator, for associated sequences, are also established parallel to those in the i.i.d. case. Various estimators in this class of estimators are contrasted, through a simulation study, among themselves and with an indirect smooth quantile estimator obtained by inverting the Poisson weights based estimator of the distribution function studied in Chaubey et al. (Statist. Probab. Lett. 81, 267–276, 2011). The indirect smoothing estimator seems to be the best estimator on account of smaller MSE, however, a quantile estimator based on the Bernstein polynomials and that using the corrected Poisson weights turn out to be almost as good as the inverse distribution function estimator using Poisson weights.
Physica A: Statistical Mechanics and its Applications, 2020
Parrondo's paradox appears in game theory which asserts that playing two losing games, A and B (s... more Parrondo's paradox appears in game theory which asserts that playing two losing games, A and B (say) randomly or periodically may result in a winning expectation. In the original paradox the strategy of game B was capital-dependent. Some extended versions of the original Parrondo's game as history dependent game, cooperative Parrondo's game and others have been introduced. In all of these methods, games are played by two players. In this paper, we introduce a generalized version of this paradox by considering three players. In our extension, two games are played among three players by throwing a three-sided dice. Each player will be in one of three places in the game. We set up the conditions for parameters under which player one is in the third place in two games A and B. Then paradoxical property is obtained by combining these two games periodically and chaotically and (s)he will be in the first place when (s)he plays the games in one of the mentioned fashions. Mathematical analysis of the generalized strategy is presented and the results are also justified by computer simulations.
Communications in Statistics - Theory and Methods, 2015
In this article we introduce an extension of Chen’s (2000) family of distributions given by Lehma... more In this article we introduce an extension of Chen’s (2000) family of distributions given by Lehman alternatives (see Gupta et al., 1998) that is shown to present another alternative to the generalized Weibull and exponentiated Weibull families for modeling survival data. The extension proposed here can be seen as the extension to the Chen’s distribution as the exponentiated Weibull is to the Weibull. A detailed analysis of the density and hazard shapes is carried out. The new model is also seen to fit well to the flood data used in fitting the exponentiated Weibull model in Mudholkar and Hutson (1996).
Statistical Science and Interdisciplinary Research, 2009
In this paper we consider analysis of two experimental data sets for evaluating lentil genotypes.... more In this paper we consider analysis of two experimental data sets for evaluating lentil genotypes. One of these data sets comes from an incomplete block design and the other one from a complete block design. The incomplete blocks contribute to the experimental error reduction and spatially correlated plot-errors can be modeled using autoregressive scheme that may lead to further improvement in the assessment of the genotypes. Such an approach was applied in several other studies to model the linear trends and spatially correlated errors. However, the assumption of a constant error variance restricts the scope of the analysis in many agricultural field trials, and in other situations in general, where heterogeneity of error variances is a reality. In this study, we have approached the problem first by fitting a model with constant error variance and generating the residuals. Using the squared residuals, we use K-cluster means technique to group the experimental units for similar squar...
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Papers by Yogendra Chaubey