Journal of Data Science

Abstract: This paper studies an effective stepwise hypotheses testing pro cedure in identifying dynamic relations between time series, and its close connection with popular information criteria such as AIC and BIC. This procedure, labeled M2, extends Chen and Lee’s (1990) procedure to cover both the strong and weak form dynamic relations; and to be used with a guided choice of significance levels which are adapting in nature. Intu itively, procedure M2 can be viewed as a backward-elimination approach that simplifies the all-possible pairwise comparisons approach implied by information criterion. New insights concerning identification of strong and weak form dynamic relations using these approaches are given. Extensive simulation experiments are conducted to illustrate the performance of the IC and M2 approach in different settings. For applications, we study the dynamic relations between price level and interest rate in US and UK, and the robustness of the model identified is also addressed.

Abstract: The study of pattern of female child birth is one of the most crucial area of human demography because it plays very important role in the building of a nation. In the present study, an attempt has been made to work-out the pattern of female child births among females belongs to different subdomains of population through the probability model and the parameters involved in the probability model under consideration has also been estimated. The suggested model, for illustration has been applied to an observed set of data taken from NFHS-III (2005-06) for the seven North East states of India known as Seven Sisters.

Abstract: This paper describes a statistical model developing from Cor respondence Analysis to date archaeological contexts of the city of Tours (France) and also to obtain an estimated absolute timescale. The data set used in the study is reported as a contingency table of ceramics against con texts. But, as pottery is not intrinsically a dating indicator (a date is rarely inscribed on each piece of pottery), we estimate dates of contexts from their finds, and we use coins to attest the date of assemblages. The model-based approach uses classical tools (correspondence analysis, linear regression and resampling methods) in an iterative scheme. Archaeologists may find in the paper a useful set of known statistical methods, while statisticians can learn a way to order well known techniques. No method is new, but their gathering is characteristic of this application

Abstract: A field study was carried out to determine the spatial distribution of air dose rate on grazed grassland after the earthquake on 11 March, 2011 in the Northwest Pacific of Northeastern Japan. Data on air dose rates (µSv h-1) were collected from Ichinoseki, south of Iwate Prefecture, Japan. Air dose rates were collected from each of 1 m interval of 12 ×12 m2 site (L-site). At the center of Lsite, 1.2 ×1.2 m2 site (S-site) was located. One hundred and forty four (144) equal spaced quadrats were defined in the S-site. Again, air dose rates were collected from central point of each of the quadrat. Moran’s I, a measure of autocorrelation was used to test the spatial heterogeneity of air dose rate on grazed grassland. Autocorrelation in S-site area was significantly higher than L-site area. Air dose rate did not show significant autocorrelation at any spatial lag in L-site. In S-site, air dose rate level showed significant autocorrelation in twelve of sixteen spatial lag. Autocorrelograms and Moran’s scatterplot showed that air dose rate was frequently and positively spatially correlated at distance less than 0.1 m.

In reliability and life-testing experiments, the researcher is often interested in the effects of extreme or varying stress factors on the lifetimes of experimental units. In this paper, a step-stress model is considered in which the life-testing experiment gets terminated either at a pre-fixed time (say, Tm+1) or at a random time ensuring at least a specified number of failures (Say, y out of n). Under this model in which the data obtained are Type-II hybrid censored, the Kumaraswamy Weibull distribution is used for the underlying lifetimes. The maximum Likelihood estimators (MLEs) of the parameters assuming a cumulative exposure model are derived. The confidence intervals of the parameters are also obtained. The hazard rate and reliability functions are estimated at usual conditions of stress. Monte Carlo simulation is carried out to investigate the precision of the maximum likelihood estimates. An application using real data is used to indicate the properties of the maximum likelihood estimators.

Most research on housing price modeling utilize linear regression models. These research mostly describe the actual contribution of factors in a linear way on magnitude, including positive or negative. The goal of this paper is to identify the non-linear patterns for 3 major types of real estates through model building that includes 49 housing factors. The datasets were composed by 33,027 transactions in Taipei City from July 2013 to the end of 2016. The non-linear patterns present in the combination manner of a sequence of uptrends and downtrends that are derived from Generalized Additive Models (GAM).

Abstract: Consider the problem of selecting independent samples from several populations for the purpose of between-group comparisons. An important aspect of the solution is the determination of clusters where mean levels are equal, often accomplished using multiple comparisons testing. We formulate the hypothesis testing problem of determining equal-mean clusters as a model selection problem. Information from all competing models is combined through Bayesian methods in an effort to provide a more realistic accounting of uncertainty. An example illustrates how the Bayesian approach leads to a logically sound presentation of multiple comparison results.

Families of distributions are commonly used to model insurance claims data that require flexible distributional forms in a satisfactory manner, but the specification problem to assess the goodness-of-fit of the hypothesized model can sometimes be a challenge due to the complexity of the likelihood function of the family of distributions involved. The previous work shows that these specification problems can be attacked by means of semi-parametric tests based on generalized method of moment (GMM) estimators. While the approach can be directly applied to both discrete and continuous families of distributions, the paper focuses on developing a testing strategy within a framework of discrete families of distributions. Both the local power analysis and the approximate slope method demonstrate the excellent performance of these tests. The finite-sample performance of the tests, based on both asymptotic and bootstrap critical values, are also discussed and are compared with established methods that require the complete specification of likelihood functions.