WHY HOLD-UPS OCCUR: THE SELF-ENFORCING RANGE OF CONTRACTUAL RELATIONSHIPS

Court enforcement and private enforcement are not alternative contract enforcement mechanisms, but are used jointly by transactors to define the self-enforcing range of a contractual relationship. Within this framework contract terms economize on the limited amounts of private enforcement capital possessed by transactors, either by directly controlling transactor behavior or by shifting private enforcement capital between transactors to coincide with likely future market conditions. Hold-ups occur when market conditions change sufficiently to place the relationship outside the self-enforcing range. This probabilistic view of hold-ups is contrasted with opportunism more generally and with moral hazard behavior.     

Tukey-Type Distributions in the Context of Financial Data

Abstract

In one-parameter (θ) families, we were not aware of explicit hypothesis testing scenarios where maximal invariant statistics failed to distinguish the models. We start with a concrete example (Sec. 2.2) to highlight such a hypothesis testing problem involving markedly different models. In this problem, because of the absence of a nontrivial uniformly most powerful invariant (UMPI) test, we briefly suggest two approaches to test the hypothesis. The first resolution (Sec. 3.1) is frequentist in nature. It utilizes a weight function on the parameter space and compares “average” distributions obtained under the null and alternative models in the sense of Wald (1947 Wald , A. ( 1947 ). Sequential Analysis . New York : Wiley . [Google Scholar] 1950 Wald , A. ( 1950 ). Statistical Decision Functions . New York : Wiley . [Google Scholar]). In contrast, a fully Bayesian resolution (Sec. 3.2) is also included. The note ends with a series of other interesting examples involving one-parameter families where maximal invariant statistics fail to distinguish the hypothesized models. The examples include easy-to-construct families of probability models involving only a single location or scale parameter θ.     

Semiparametric estimation of plane similarities: application to fast computation of aeronautic loads

In the big data era, it is often needed to resolve the problem of parsimonious data representation. In this paper, the data under study are curves and the sparse representation is based on a semiparametric model. Indeed, we propose an original registration model for noisy curves. The model is built transforming an unknown function by plane similarities. We develop a statistical method that allows to estimate the parameters characterizing the plane similarities. The properties of the statistical procedure are studied. We show the convergence and the asymptotic normality of the estimators. Numerical simulations and a real-life aeronautic example illustrate and demonstrate the strength of our methodology.     

Money and Happiness: Income,Wealth and Subjective Well-Being

Social Indicators Research - We examine the complex relationship between money and happiness. We find that both permanent income and wealth are better predictors of life satisfaction than current...     

Extended pairwise local alignment of wild card DNA/RNA sequences using dynamic programming

Optimal string alignment is used to discover evolutionary relationships or mutations in DNA/RNA or protein sequences. Errors, missing parts or uncertainty in such a sequence can be covered with wild cards, so-called wild bases. This makes an alignment possible even when the data are corrupted or incomplete. The extended pairwise local alignment of wild card DNA/RNA sequences requires additional calculations in the dynamic programming algorithm and necessitates a subsequent best- and worst-case analysis for the wild card positions. In this paper, we propose an algorithm which solves the problem of input data wild cards, offers a highly flexible set of parameters and displays a detailed alignment output and a compact representation of the mutated positions of the alignment. An implementation of the algorithm can be obtained at https://github.com/sysbio-bioinf/swat+ and http://sysbio.uni-ulm.de/?Software:Swat+.