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We consider a few online and offline games under actual playing conditions. Generally it is expected that initially a player obtains additional skill with experience of playing more number of games and then it should finally saturate. This phase is identified when a player, with the experience of very few games, loses more when she plays against players with much longer history. Then the winning proportion curve moves up and finally it saturates. We benchmark our analysis and discussion against Chess, the most skilled one among the games we consider here. We use proprietary data from actual games (online and offline) as well as experiments for our statistical analysis. In this regard, we show that Rummy has stronger skill and learning effects. Ludo has similar characteristics as Rummy, but at a weaker level. Similarly, a game that is perceived as almost no skill such as Teen Patti indeed presents much less skill in the analysis. In the next section we describe the game structures., Comment: 25 pages, 9 figures

The objective of this study is applying a utility based analysis to a comparatively efficient design experiment which can capture people's perception towards the various components of a commodity. Here we studied the multi-dimensional poverty index and the relative importance of its components and their two-factor interaction effects. We also discussed how to model a choice based conjoint data for determining the utility of the components and their interactions. Empirical results from survey data shows the nature of coefficients, in terms of utility derived by the individuals, their statistical significance and validity in the present framework. There has been some discrepancies in the results between the bootstrap model and the original model, which can be understood by surveying more people, and ensuring comparative homogeneity in the data.

This paper examines two different variants of the Ludo game, involving multiple dice and a fixed number of total turns. Within each variant, multiple game lengths (total no. of turns) are considered. To compare the two variants, a set of intuitive, rule-based strategies is designed, representing different broad methods of strategic play. Game play is simulated between bots (automated software applications executing repetitive tasks over a network) following these strategies. The expected results are computed using certain game theoretic and probabilistic explanations, helping to understand the performance of the different strategies. The different strategies are further analyzed using win percentage in a large number of simulations, and Nash Equilibrium strategies are computed for both variants for a varying number of total turns. The Nash Equilibrium strategies across different game lengths are compared. A clear distinction between performances of strategies is observed, with more sophisticated strategies beating the naive one. A gradual shift in optimal strategy profiles is observed with changing game length, and certain sophisticated strategies even confound each other's performance while playing against each other., Comment: 28 pages, 9 figures

The system of Virtual Try-ON (VTON) allows a user to try a product virtually. In general, a VTON system takes a clothing source and a person's image to predict the try-on output of the person in the given clothing. Although existing methods perform well for simple poses, in case of bent or crossed arms posture or when there is a significant difference between the alignment of the source clothing and the pose of the target person, these methods fail by generating inaccurate clothing deformations. In the VTON methods that employ Thin Plate Spline (TPS) based clothing transformations, this mainly occurs for two reasons - (1)~the second-order smoothness constraint of TPS that restricts the bending of the object plane. (2)~Overlaps among different clothing parts (e.g., sleeves and torso) can not be modeled by a single TPS transformation, as it assumes the clothing as a single planar object; therefore, disregards the independence of movement of different clothing parts. To this end, we make two major contributions. Concerning the bending limitations of TPS, we propose a human AnaTomy-Aware Geometric (ATAG) transformation. Regarding the overlap issue, we propose a part-based warping approach that divides the clothing into independently warpable parts to warp them separately and later combine them. Extensive analysis shows the efficacy of this approach., Comment: arXiv admin note: substantial text overlap with arXiv:2208.08076

Analysis and estimation of consumer expenditure and budget shares are important for understanding quantitatively the expenditure based behaviour of the people of a country or region. The costs attached with performing consumer expenditure and budget shares survey are significant. These surveys are quite time consuming and even a slight increase in sample size could lead to a significant increase in the cost of the survey under the current structure. This high cost for the survey also reduces its flexibility. Hence it is of paramount importance to be able to provide a statistically sound and relatively cheaper facilitation method of survey to be able to understand the consumption expenditure distribution of a country or a region without much loss of inferential power. In the context of the Indian National Sample Survey, in this paper we perform analysis and estimation of consumer expenditure assuming uniform prices across First Stage Units (FSUs) of a sampling design, and check its feasibility in estimating the consumer expenditure distribution of the country. We also compare it with the existing methodology and infer that there is no significant loss of information in the estimated consumer expenditure distribution and other inferences like the Lorenz curve and the Gini Index, when uniform distribution of prices within any FSU is assumed.

The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an user the convenience of trying a clothing at the comfort of their home. In general, most of the existing VTON methods produce inconsistent results when a person posing with his arms folded i.e., bent or crossed, wants to try an outfit. The problem becomes severe in the case of long-sleeved outfits. As then, for crossed arm postures, overlap among different clothing parts might happen. The existing approaches, especially the warping-based methods employing \textit{Thin Plate Spline (TPS)} transform can not tackle such cases. To this end, we attempt a solution approach where the clothing from the source person is segmented into semantically meaningful parts and each part is warped independently to the shape of the person. To address the bending issue, we employ hand-crafted geometric features consistent with human body geometry for warping the source outfit. In addition, we propose two learning-based modules: a synthesizer network and a mask prediction network. All these together attempt to produce a photo-realistic, pose-robust VTON solution without requiring any paired training data. Comparison with some of the benchmark methods clearly establishes the effectiveness of the approach.

In the post-crisis era, financial regulators and policymakers are increasingly interested in data-driven tools to measure systemic risk and to identify systemically important firms. Granger Causality (GC) based techniques to build networks among financial firms using time series of their stock returns have received significant attention in recent years. Existing GC network methods model conditional means, and do not distinguish between connectivity in lower and upper tails of the return distribution - an aspect crucial for systemic risk analysis. We propose statistical methods that measure connectivity in the financial sector using system-wide tail-based analysis and is able to distinguish between connectivity in lower and upper tails of the return distribution. This is achieved using bivariate and multivariate GC analysis based on regular and Lasso penalized quantile regressions, an approach we call quantile Granger causality (QGC). By considering centrality measures of these financial networks, we can assess the build-up of systemic risk and identify risk propagation channels. We provide an asymptotic theory of QGC estimators under a quantile vector autoregressive model, and show its benefit over regular GC analysis on simulated data. We apply our method to the monthly stock returns of large U.S. firms and demonstrate that lower tail based networks can detect systemically risky periods in historical data with higher accuracy than mean-based networks. In a similar analysis of large Indian banks, we find that upper and lower tail networks convey different information and have the potential to distinguish between periods of high connectivity that are governed by positive vs negative news in the market.

For about 125 items of food, the Consumer Expenditure Survey (CES) schedule of the Indian National Sample Survey asks the interviewer to obtain both quantity and value of household consumption during the reference period from the respondent. This would appear to put a great burden on the respondent. But it is likely that the price usually paid is almost the same within each first stage unit (fsu). The present work proposes a new sampling scheme to estimate demand elasticities of essential food items. While the conventional sampling method used in practice (e.g. in NSS consumer expenditure survey) involves seeking price information from many households sampled from a fsu, the proposed procedure involves only one household chosen randomly from every fsu for price data collection and thus requires much less interview burden. Using unit records for vegetable items in the NSS's 2011-12 CES, our results show that in spite of requiring much less data, the new scheme captures the household food consumption behavior as precisely as before.

This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigenvalue and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for Barndorff-Nielsen and Shephard model used in financial markets. We consider multiple assets in the portfolio for theoretical purpose and demonstrate our approach with numerical examples taking three stocks in the portfolio. The results obtained in this paper have important implications for the commodity sector where such swaps would be useful for hedging risk., Comment: Accepted in International Journal of Financial Engineering

With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of a person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the time and effort which is generally required for changing the skin tone using existing applications (e.g., Photoshop) by professionals or novice. To establish the efficacy of this method we have compared our result with that of some popular photo editor and also with the result of some existing benchmark method related to human attribute manipulation. Rigorous experiments on different datasets show the effectiveness of this method in terms of synthesizing perceptually convincing outputs., Comment: Accepted in International Conference on Pattern Recognition 2020

Tea auctions across India occur as an ascending open auction, conducted online. Before the auction, a sample of the tea lot is sent to potential bidders and a group of tea tasters. The seller's reserve price is a confidential function of the tea taster's valuation, which also possibly acts as a signal to the bidders. In this paper, we work with the dataset from a single tea auction house, J Thomas, of tea dust category, on 49 weeks in the time span of 2018-2019, with the following objectives in mind: $\bullet$ Objective classification of the various categories of tea dust (25) into a more manageable, and robust classification of the tea dust, based on source and grades. $\bullet$ Predict which tea lots would be sold in the auction market, and a model for the final price conditioned on sale. $\bullet$ To study the distribution of price and ratio of the sold tea auction lots. $\bullet$ Make a detailed analysis of the information obtained from the tea taster's valuation and its impact on the final auction price. The model used has shown various promising results on cross-validation. The importance of valuation is firmly established through analysis of causal relationship between the valuation and the actual price. The authors hope that this study of the properties and the detailed analysis of the role played by the various factors, would be significant in the decision making process for the players of the auction game, pave the way to remove the manual interference in an attempt to automate the auction procedure, and improve tea quality in markets.

We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumption of discretized geometric movement of the stock prices. We consider two different cost functions where the first function involves just the fiscal cost while the cost function of the second kind incorporates the risks of non-strategic constrained investments along with fiscal costs. Precisely, the strategic development of constrained execution of K stocks within a stipulated time frame of T units is established mathematically using a well-defined stochastic behaviour of stock prices and the same is compared with some of the commonly-used execution strategies using the historical stock price data.

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to managing the Value at Risk (VaR) assuming a heavy tailed distribution of the stock prices return. We use a stochastic maximum principle to formulate the dynamic optimisation problem. The equations which we obtain does not have any explicit analytical solution, so we look for accurate approximations to estimate the value function and optimal strategy. As our calibration strategy is non-parametric in nature, no prior knowledge on the form of the distribution function is needed. We also provide detailed empirical illustration using real life data. Our results show close concordance with financial intuition.We expect that our results will add to the arsenal of the high frequency traders., Comment: Minor revision in Stochastic Analysis and Applications

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to the Value at Risk assuming a heavy tail distribution of the stock prices return. We use Markov Decision Process and dynamic programming principle to get the optimal strategies and the value function which maximize the expected utility for parametric as well as non parametric distributions. Due to lack of explicit solution in the non parametric case, we use numerical integration for optimization, Comment: Published in International Journal Mathematical Modelling and Numerical Optimisation, Vol. 10, No. 4, 2020

This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigen-value and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for financial markets with Markov-modulated volatilities. We consider multiple assets in the portfolio for theoretical purpose and demonstrate our approach with numerical examples taking three stocks in the portfolio. The resultsobtained in this paper have important implications for the commodity sector where such swaps would be useful for hedging risk, Comment: arXiv admin note: text overlap with arXiv:1205.5565 by other authors

Efficient estimation of population size from dependent dual-record system (DRS) remains a statistical challenge in capture-recapture type experiment. Owing to the nonidentifiability of the suitable Time-Behavioral Response Variation model (denoted as $M_{tb}$) under DRS, few methods are developed in Bayesian paradigm based on informative priors. Our contribution in this article is in developing integrated likelihood function from model $M_{tb}$ based on a novel approach developed by Severini (2007, Biometrika). Suitable weight function on nuisance parameter is derived under the assumption of availability of knowledge on the direction of behavioral dependency. Such pseudo-likelihood function is constructed so that the resulting estimator possess some desirable properties including invariance and negligible prior (or weight) sensitiveness. Extensive simulations explore the better performance of our proposed method in most of the situations than the existing Bayesian methods. Moreover, being a non-Bayesian estimator, it simply avoids heavy computational effort and time. Finally, illustration based on two real life data sets on epidemiology and economic census are presented., Comment: 19 Pages

The objective of this paper is to study the characteristics (geometric and otherwise) of very large attribute based undirected networks. Real-world networks are often very large and fast evolving. Their analysis and understanding present a great challenge. An Attribute based network is a graph in which the edges depend on certain properties of the vertices on which they are incident. In context of a social network, the existence of links between two individuals may depend on certain attributes of the two of them. We use the Lovasz type sampling strategy of observing a certain random process on a graph locally , i.e., in the neighborhood of a node, and deriving information about global properties of the graph. The corresponding adjacency matrix is our primary object of interest. We study the efficiency of recently proposed sampling strategies, modified to our set up, to estimate the degree distribution, centrality measures, planarity etc. The limiting distributions are derived using recently developed probabilistic techniques for random matrices and hence we devise relevant test statistics and confidence intervals for different parameters / hypotheses of interest. We hope that our work will be useful for social and computer scientists for designing sampling strategies and computational algorithms appropriate to their respective domains of inquiry. Extensive simulations studies are done to empirically verify the probabilistic statements made in the paper., Comment: Working Paper (To be presented in LSCNA, in conjunction with IEEE ANTS, ISI Kolkata)

In many countries information on expectations collected through consumer confidence surveys are used in macroeconomic policy formulation. Unfortunately, before doing so, the consistency of responses is often not taken into account, leading to biases creeping in and affecting the reliability of the indices hence created. This paper describes how latent class analysis may be used to check the consistency of responses and ensure a parsimonious questionnaire. In particular, we examine how temporal changes may be incorporated into the model. Our methodology is illustrated using three rounds of Consumer Confidence Survey (CCS) conducted by Reserve Bank of India (RBI).

Motivated by various applications, we consider the problem of homogeneous human population size (N) estimation from Dual-record system (DRS) (equivalently, two-sample capture-recapture experiment). The likelihood estimate from the independent capture-recapture model Mt is widely used in this context though appropriateness of the behavioral dependence model Mtb is unanimously acknowledged. Our primary aim is to investigate the use of several relevant pseudo-likelihood methods profiling N, explicitly for model Mtb. An adjustment over profile likelihood is proposed. Simulation studies are carried out to evaluate the performance of the proposed method compared with Bayes estimate suggested for general capture-recapture experiment by Lee et al. (Statistica Sinica, 2003, vol. 13). We also analyse the effect of possible model mis-specification, due to the use of model Mt, in terms of efficiency and robustness. Finally two real life examples with different characteristics are presented for illustration of the methodologies discussed., Comment: 24 Pages, 4 Figures

Dual-record system (DRS) (equivalently two sample Capture-recapture experiment) model with time and behavioral response variation, has attracted much attention specifically in the domain of Official Statistics and Epidemiology. The relevant model suffers from parameter identifiability problem and proper Bayesian methodologies could be helpful to overcome the situation. In this article, we have formulated the population size estimation problem in DRS as a missing data analysis under both the known and unknown directional nature of underlying behavioral response effect. Two simple empirical Bayes approaches are proposed and investigated their performances for this complex model along with a fully Bayes treatment. Extensive simulation studies are carried out to compare the performances of these competitive approaches and a real data example is also illustrated. Finally, some features of these methods and recommendations to implement them in practice are explored depending upon the availability of knowledge on the nature of behavioral response effect., Comment: 25 pages, 3 figures

For Dual-record system, in the context of human population, the popular Chandrasekar-Deming model incorporates only the time variation effect on capture probabilities. How-ever, in practice population may undergo behavioral change after being captured first time. In this paper we focus on the Dual-record system model (equivalent to capture- recapture model with two sampling occasions) with both the time as well as behavioral response variation. The relevant model suffers from identifiability problem. Two approaches are proposed from which approximate Bayes estimates can be obtained using very simple Gibbs sampling strategies. We explore the features of our two proposed methods and their usages depending on the availability (or non-availability) of the information on the nature of behavioral response effect. Extensive simulation studies are carried out to evaluate their performances and compare with few available approaches. Finally, a real data application is provided to the model and the methods., Comment: 20 pages and 2 figures