Shannon information in record data

In this article we present results on the Shannon information (SI) contained in upper (lower) record values and associated record times in a sequence of i.i.d continuous variables. We then establish an interesting relationship between the SI content of a random sample of fixed size, and the SI in the data consisting of sequential maxima. We also consider the information contained in the record data from an inverse sampling plan (ISP).     

On the nearness of record values to order statistics from Pitman’s measure of closeness

In the two-sample prediction problem, record values from the present sample may be used as predictors of order statistics from a future sample. In this paper, we investigate the nearness of record statistics (upper and lower) to order statistics from a location-scale family of distributions in the sense of Pitman closeness and discuss the corresponding monotonicity properties. We then determine the closest record value to a specific order statistic from a future sample. Even though in general it depends on the parent distribution, exact and explicit expressions are derived for the required probabilities in the case of exponential and uniform distributions, and some computational results are presented as well. Finally, we consider the mean squared error criterion and examine the corresponding results in the exponential case.     

Further Evidence on the Statistical Properties of Real GNP*

The well‐known lack of power of unit‐root tests has often been attributed to the short length of macroeconomic variables and also to data‐generating processes (DGPs) departing from the I(1)–I(0) models. This paper shows that by using long spans of annual real gross national product (GNP) and GNP per capita (133 years), high power can be achieved, leading to the rejection of both the unit‐root and the trend‐stationary hypothesis. More flexible representations are then considered, namely, processes containing structural breaks (SB) and fractional orders of integration (FI). Economic justification for the presence of these features in GNP is provided. It is shown that both FI and SB formulations are in general preferred to the autoregressive integrated moving average (ARIMA) [I(1) or I(0)] formulations. As a novelty in this literature, new techniques are applied to discriminate between FI and SB. It turns out that the FI specification is preferred, implying that GNP and GNP per capita are non‐stationary, highly persistent but mean‐reverting series. Finally, it is shown that the results are robust when breaks in the deterministic component are allowed for in the FI model. Some macroeconomic implications of these findings are also discussed.     

Fisher information in order statistics and their concomitants in bivariate censored samples

We evaluate the Fisher information (FI) contained in a collection of order statistics and their concomitants from a bivariate random sample. Special attention is given to Type II censored samples. We present a general decomposition result and recurrence relations that are useful in finding the FI in all types of censored samples. We also obtain some asymptotic results for the FI. For the bivariate normal parent, we obtain explicit and asymptotic expressions for the elements of the FI matrix for Type II censored samples. We discuss implications of our findings on inference on the bivariate normal parameters, especially on the correlation. The first author’s research was supported in part by National Institutes of Health, USA, Grant # M01 RR00034 and the second author’s research was supported by a training grant from the Egyptian government     

An Experimental Test of the Robustness and the Power of Forward Induction

Existing experimental studies tested the forward induction (FI) prediction, an equilibrium selection criterion, in a battle‐of‐the‐sexes game and found limited support due to a confounding focal point. The focal point arises from an asymmetrically offered outside option in the first stage of the game. The robustness of FI to different first‐stage asymmetries, however, is still unknown. To investigate this issue, the paper sheds light on the interplay of FI and (i) payoff asymmetries and (ii) endogenously generated asymmetries (in a game that is initially fully symmetric). The results from a laboratory experiment confirm the robustness and power of the FI prediction. Copyright © 2013 John Wiley & Sons, Ltd.     

On the Fisher information in record values

In this article, we establish some general results concerning the comparison of the amount of the Fisher information contained in n record values with the Fisher information contained in n iid observations from the original distribution. Some common distributions are classified according to this criterion. We also propose some methods of estimation based on record values. The results may be of interest in some life testing problems. Received: September 1999     

Characterizations of probability distributions via bivariate regression of record values

Bairamov et al. (Aust N Z J Stat 47:543–547, 2005) characterize the exponential distribution in terms of the regression of a function of a record value with its adjacent record values as covariates. We extend these results to the case of non-adjacent covariates. We also consider a more general setting involving monotone transformations. As special cases, we present characterizations involving weighted arithmetic, geometric, and harmonic means.     

An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics

In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ?ⁿ, n ∈ ?. Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain; (ii) the tendency to cluster at certain outcome values; and (iii) contemporaneous dependence. These kinds of properties can be found for high‐ or ultra‐high‐frequency data describing the trading process on financial markets. We present a straightforward sampling method for such an inflated multivariate density through the application of an independence Metropolis–Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivariate density of bid and ask quote changes in a high‐frequency setup. We show how to derive the implied conditional discrete density of the bid–ask spread, taking quote clusterings (at multiples of 5 ticks) into account. Copyright © 2009 John Wiley & Sons, Ltd.     

Measuring Identification Risk in Microdata Release and Its Control by Post‐randomisation

Statistical agencies often release a masked or perturbed version of survey data to protect the confidentiality of respondents' information. Ideally, a perturbation procedure should provide confidentiality protection without much loss of data quality, so that the released data may practically be treated as original data for making inferences. One major objective is to control the risk of correctly identifying any respondent's records in released data, by matching the values of some identifying or key variables. For categorical key variables, we propose a new approach to measuring identification risk and setting strict disclosure control goals. The general idea is to ensure that the probability of correctly identifying any respondent or surveyed unit is at most ξ, which is pre‐specified. Then, we develop an unbiased post‐randomisation procedure that achieves this goal for ξ>1/3. The procedure allows substantial control over possible changes to the original data, and the variance it induces is of a lower order of magnitude than sampling variance. We apply the procedure to a real data set, where it performs consistently with the theoretical results and quite importantly, shows very little data quality loss.     

Subsampling Inference with K Populations and a Non‐standard Behrens–Fisher Problem

We revisit the methodology and historical development of subsampling, and then explore in detail its use in hypothesis testing, an area which has received surprisingly modest attention. In particular, the general set‐up of a possibly high‐dimensional parameter with data from K populations is explored. The role of centring the subsampling distribution is highlighted, and it is shown that hypothesis testing with a data‐centred subsampling distribution is more powerful. In addition we demonstrate subsampling’s ability to handle a non‐standard Behrens–Fisher problem, i.e., a comparison of the means of two or more populations which may possess not only different and possibly infinite variances, but may also possess different distributions. However, our formulation is general, permitting even functional data and/or statistics. Finally, we provide theory for K ‐ sample U ‐ statistics that helps establish the asymptotic validity of subsampling confidence intervals and tests in this very general setting.     

A Simulation Approach to Nonparametric Empirical Bayes Analysis

We deal with general mixture of hierarchical models of the form m(x) = f_øf(x |θ) g (θ)dθ , where g(θ) and m(x) are called mixing and mixed or compound densities respectively, and θ is called the mixing parameter. The usual statistical application of these models emerges when we have data x_i, i = 1,…,n with densities f(x_i|θ_i) for given θ_i, and the θ₁ are independent with common density g(θ) . For a certain well known class of densities f(x |θ) , we present a sample-based approach to reconstruct g(θ) . We first provide theoretical results and then we use, in an empirical Bayes spirit, the first four moments of the data to estimate the first four moments of g(θ) . By using sampling techniques we proceed in a fully Bayesian fashion to obtain any posterior summaries of interest. Simulations which investigate the operating characteristics of our proposed methodology are presented. We illustrate our approach using data from mixed Poisson and mixed exponential densities.     

Selecting the best regression equation via the P-value of F-test

In this article, we establish a method of selecting the best regression equation on the basis of F-test. The basic idea is to select the most significant regression equation, which corresponds to the minimum P-value of F-test. We also present upper and lower bounds for the P-value together with approximations for these bounds which are simple to compute. Using this method, not only can we find out the best regression equation, we also obtain its significance probability. When the method is applied to the well-known data from Hald (1952) and Gorman & Toman (1966), the results are satisfactory.     

Admissible unbiased variance estimation in finite population sampling under randomized response

LetP be the proportion of units in a finite population possessing a sensitive attribute. We prove the admissibility of (i) an unbiased estimator of the variance of a general homogeneous linear unbiased estimator ofP and (ii) an unbiased estimator of the population varianceP(1−P), based on an arbitrary but fixed sampling design, under the randomized response plans due to Warner (1965) and Eriksson (1973). Admissibility of an unbiased strategy for estimating the population variance is also established.     

Post-Earnings Announcement Drift in Spain and Behavioural Finance Models

Our study examines whether behavioural theories can explain post-earnings announcement drift (i.e. earnings momentum) in the Spanish market. In particular, we test models proposed by Barberis et al. (Journal of Financial Economics, 49, pp. 307–343, 1998), Daniel et al. ( Journal of Finance, 53(6), pp. 1839–1885, 1998) and Hong and Stein (Journal of Finance, 54(6), pp. 2143–2184, 1999). Each of these behavioural models draws on two premises – cognitive biases and limits to arbitrage – that we assume will vary with a given country's cultural and institutional features. Therefore, we must exercise caution when extrapolating the favourable results observed in the US market to markets outside of the USA. Our results provide little evidence in support of the hypothesis used to test whether these models can indeed explain the earnings momentum anomaly in the Spanish market. We believe some characteristics of the Spanish market, such as its lower score on the Individualism Index, lower levels of investor protection and code-law-based legal system, may explain why our results differ from those obtained in the USA.     

Bounds on expectations of small order statistics from decreasing density populations

For i > (n + 1)/2, Danielak (Statistics 37:305–324, 2003) established an optimal positive upper mean-variance bound on the expectation of ith order statistic based on the i.i.d. sample of size n from the decreasing density population. We show that the best bounds on the expected deviation of the ith order statistics from the population mean, i ≤ (n + 1)/2, expressed in more general scale units generated by pth absolute central moments with p > 1 amount to zero. We also determine the respective strictly negative bounds in the mean absolute deviation units.     

The mean residual of record values at the level of previous records

In the study of reliability of the technical systems, records model plays an important role. Suppose that a technical system is subject to shocks, e.g. peaks of voltages or stresses. The successive large shocks may be viewed as realizations of records from a sequence of identically independent voltages. Assume that the lower limit value of the mth record voltage (stress) is v > 0. Under these conditions, we propose a mean residual of records (MRR’s) for the future records. We study several properties of MRR. We show that the proposed MRR uniquely characterizes the distribution function that generated the sequence of records. It is proved that when the model under study has an increasing hazard rate the corresponding MRR is decreasing. We also compare between two record systems based on their MRR’s when these systems are ordered in terms of their hazard rates.     

An exact non-iterative sampling procedure for discrete missing data problems

Many statistical problems can be formulated as discrete missing data problems (MDPs). Examples include change-point problems, capture and recapture models, sample survey with non-response, zero-inflated Poisson models, medical screening/diagnostic tests and bioassay. This paper proposes an exact non-iterative sampling algorithm to obtain independently and identically distributed (i.i.d.) samples from posterior distribution in discrete MDPs. The new algorithm is essentially a conditional sampling, thus completely avoiding problems of convergence and slow convergence in iterative algorithms such as Markov chain Monte Carlo. Different from the general inverse Bayes formulae (IBF) sampler of Tan, Tian and Ng (Statistica Sinica, 13 , 2003, 625), the implementation of the new algorithm requires neither the expectation maximization nor the sampling importance resampling algorithms. The key idea is to first utilize the sampling-wise IBF to derive the conditional distribution of the missing data given the observed data, and then to draw i.i.d. samples from the complete-data posterior distribution. We first illustrate the method with a performing example and then apply the method to contingency tables with one supplemental margin for an human immunodeficiency virus study.     

On Invariant Post‐randomization for Statistical Disclosure Control

In this paper, we investigate certain operational and inferential aspects of invariant Post‐randomization Method (PRAM) as a tool for disclosure limitation of categorical data. Invariant PRAM preserves unbiasedness of certain estimators, but inflates their variances and distorts other attributes. We introduce the concept of strongly invariant PRAM, which does not affect data utility or the properties of any statistical method. However, the procedure seems feasible in limited situations. We review methods for constructing invariant PRAM matrices and prove that a conditional approach, which can preserve the original data on any subset of variables, yields invariant PRAM. For multinomial sampling, we derive expressions for variance inflation inflicted by invariant PRAM and variances of certain estimators of the cell probabilities and also their tight upper bounds. We discuss estimation of these quantities and thereby assessing statistical efficiency loss from applying invariant PRAM. We find a connection between invariant PRAM and creating partially synthetic data using a non‐parametric approach, and compare estimation variance under the two approaches. Finally, we discuss some aspects of invariant PRAM in a general survey context.     

An invariance principle forU-statistics in simple random sampling without replacement

Summary Supplementing the well-known invariance principle forU-statistics based on i.i.d. observations (Miller and Sen 1972) we establish an invariance principle forU-statistics in case of simple random sampling from a sequence of finite populations. This generalizes the asymptotic normality-result of Nandi and Sen (1963), and permits e.g. to prove asymptotic normality ofU-statistics in the presence of random non-response.