FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

Estimating function method for product autoregressive models

The first-order product autoregressive (PAR(1)) model introduced by McKenzie in 1982 McKenzie, E. D. (1982). Product autoregression: A time series characterization of the gamma distribution. Journal of Applied Probability 19:463–468. [Google Scholar] did not attract the attention of practitioners due to the unavailability of a proper estimation method. This article proposes an estimating function (EF) method to fill the gap. In particular, we suggest an optimal combination of linear and quadratic EFs to overcome the problem of parameter identification. The procedure is applied to Weibull and Gamma PAR(1) models. Simulation and data analysis show that the proposed method performs better than the existing methods.     

Effects of some design factors on the distribution of similarity indices in cluster analysis

This article investigates the effects of number of clusters, cluster size, and correction for chance agreement on the distribution of two similarity indices, namely, Jaccard and Rand indices. Skewness and kurtosis are calculated for the two indices and their corrected forms then compared with those of the normal distribution. Three clustering algorithms are implemented: complete linkage, Ward, and K-means. Data were randomly generated from bivariate normal distributions with specified means and variance covariance matrices. Three-way ANOVA is performed to assess the significance of the design factors using skewness and kurtosis of the indices as responses. Test statistics for testing skewness and kurtosis and observed power are calculated. Simulation results showed that independent of the clustering algorithms or the similarity indices used, the interaction effect cluster size x number of clusters and the main effects of cluster size and number of clusters were found always significant for skewness and kurtosis. The three way interaction of cluster size x correction x number of clusters was significant for skewness of Rand and Jaccard indices using all clustering algorithms, but was not significant using Ward's method for both Rand and Jaccard indices, while significant for Jaccard only using complete linkage and K-means algorithms. The correction for chance agreement was significant for skewness and kurtosis using Rand and Jaccard indices when complete linkage method is used. Hence, such design factors must be taken into consideration when studying distribution of such indices.     

Weighted kappa as a function of unweighted kappas

The kappa coefficient is a widely used measure for assessing agreement on a nominal scale. Weighted kappa is an extension of Cohen's kappa that is commonly used for measuring agreement on an ordinal scale. In this article, it is shown that weighted kappa can be computed as a function of unweighted kappas. The latter coefficients are kappa coefficients that correspond to smaller contingency tables that are obtained by merging categories.     

Bayesian prediction of order statistics with fixed and random sample sizes based on k-record values from Pareto distribution

In this paper, the two-parameter Pareto distribution is considered and the problem of prediction of order statistics from a future sample and that of its geometric mean are discussed. The Bayesian approach is applied to construct predictors based on observed k-record values for the cases when the future sample size is fixed and when it is random. Several Bayesian prediction intervals are derived. Finally, the results of a simulation study and a numerical example are presented for illustrating all the inferential procedures developed here.     

On fiducial generators

Fiducial inference has been gaining presence recently and it is the intention of the present article to look at the notion of fiducial generators; meaning procedures to simulate parameter values that in some sense correspond to simulations from some implicit fiducial distribution. It is well known that when the distribution has group structure, stemming from the natural pivotal associated, a fiducial may be obtained. It is in the non group distributions that there appears to be still room for finding a fiducial distribution. Recently some general procedures have been proposed for dealing with generalized fiducials, but these depend on certain choices for a structural equation or a fiducial equation, as in Hannig (2009 Hannig, J. (2009). On generalized fiducial inference. Stat. Sin. 19:491–544.[Web of Science ®] , [Google Scholar]) or Taraldsen and Lindqvist (2013 Taraldsen, G., Lindqvist, B.H. (2013). Fiducial theory and optimal inference. Ann. Stat. 41(1):323–341.[Crossref], [Web of Science ®] , [Google Scholar]), respectively. A brief presentation is made of an earlier approach to fiducial inference for multivariate parameters, as in Brillinger (1962 Brillinger, D.R. (1962). Examples bearing on the definition of fiducial probability with a bibliography. Ann. Math. Stat. 33(4):1349–1355.[Crossref] , [Google Scholar]), and the implied fiducial generator introduced in Engen and Lillegård (1997 Engen, S., Lillegård, M. (1997). Stochastic simulation conditioned on sufficient statistics. Biometrika 84(1):235–240.[Crossref], [Web of Science ®] , [Google Scholar]), trying to connect them. Three interesting non group distributions are seen; two of them, the truncated exponential and the two-parameter gamma, already reported in literature. A third non group distribution is analyzed; the inverse Gaussian, connecting the fiducial that results following Brillinger (1962 Brillinger, D.R. (1962). Examples bearing on the definition of fiducial probability with a bibliography. Ann. Math. Stat. 33(4):1349–1355.[Crossref] , [Google Scholar]), with a result pertaining confidence limits for the shape parameter in Hsieh (1990 Hsieh, H.K. (1990). Inferences on the coefficient of variation of an inverse-Gaussian distribution. Commun. Stat. - Theory Methods 19(5):1589–1605.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). In the three cases, comparisons are made with the Bayesian posteriors that have been known to be close numerically. Some discussion is made on the issue of singularities of the fiducial density and its connection with densities that do not integrate to unity. As to the case of discrete observables, some comments are made for the Bernoulli distribution, only.     

International Migrant Remittances and Labour Supply in Nigeria

The study analyses how remittances to Nigeria affect the labour supply of recipients using Propensity Score Matching (PSM) and a Log‐Linear regression model, with data from the 2013 Nigerian General Household Survey. The PSM results show that for the entire sample, the difference between the average amount of labour supplied per week by those that receive remittances and the amount they would have supplied without remittances is insignificant. The marginal impact analysis also shows that, ceteris paribus, the average labour supply for all recipients is inelastic to remittances. The results from the sub‐group analysis, however, show that receiving remittances negatively affects the labour supply of the self‐employed in agriculture, teenagers and the elderly. These results led us to the recommendation that policies to increase the inflow of remittances should be encouraged but in tandem with programmes to educate farmers on the benefit of investing remittances received in their farming business.     

Residential Remittances and Food Security in the Upper West Region of Ghana

In recent years, out migration from the Upper West Region to the southern belt of Ghana for farming has become commonplace. The natural question that has arisen is: what is the potential impact of remittances from this migration pattern on food security in the region? Using multivariate ordered logistic regression this study assesses the linkage between remittances and household food security (derived using the HFIAS) among urban and rural households (n=1,438) in the region. The findings show that urban remittance‐receiving households and rural remittance and non‐remittance receiving households were more likely (OR=2.44, p<0.05; OR=2.46, p<0.001; and OR=1.49, p<0.1, respectively) to report being more severely food‐insecure than urban non‐remittance receiving households. The findings demonstrate that household strategies such as migration and remittances on their own are not sufficient to ameliorate the precarious food insecurity situation of the region. The study calls for development of alternative livelihoods in the region.     

Happily Religious: The Surprising Sources of Happiness Among Lesbian,Gay, Bisexual,and Transgender Adults

This article analyzes the impact of religion on reported levels of subjective well‐being (general happiness) among lesbian, gay, bisexual, and transgender (LGBT) adults. Although previous studies find religious affiliation to be a significant predictor of subjective well‐being among the general population in the United States, limited quantitative research investigates general happiness among sexual and gender minorities. This study augments the existing literature by using a national survey of LGBT adults conducted by the Pew Research Center in 2013. The results show that religious affiliation is a significant predictor of LGBT individuals’ happiness. LGBT individuals who identify as Catholic, agnostic or atheist, or with no particular religious affiliation report lower levels of happiness compared to mainline Protestants. Surprisingly, no significant differences are found between mainline Protestants (whose church doctrine often accepts same‐sex relations) and evangelical Protestants (whose church doctrine often condemns same‐sex relations). In addition, income is the only control variable that affects general happiness. Our analysis reveals interesting differences in the determinants of subjective well‐being between the LGBT and general population.