Estimation of river and stream temperature trends under haphazard sampling

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.     

Optimal superimposed codes and designs for Renyi''s search model

Renyi (Bull. Amer. Math. Soc. 71 (6) (1965) 809) suggested a combinatorial group testing model, in which the size of a testing group was restricted. In this model, Renyi considered the search of one defective element (significant factor) from the finite set of elements (factors). The corresponding optimal search designs were obtained by Katona (J. Combin. Theory 1 (2) (1966) 174). In the present work, we study Renyi's search model of several significant factors. This problem is closely related to the concept of binary superimposed codes, which were introduced by Kautz and Singleton (IEEE Trans. Inform Theory 10 (4) (1964) 363) and were investigated by D'yachkov and Rykov (Problems Control Inform. Theory 12 (4) (1983) 229), Erdos et al. (Israel J. Math. 51 (1–2) (1985) 75), Ruszinko (J. Combin. Theory Ser. A 66 (1994) 302) and Furedi (J. Combin. Theory Ser. A 73 (1996) 172). Our goal is to prove a lower bound on the search length and to construct the optimal superimposed codes and search designs. The preliminary results have been published by D'yachkov and Rykov (Conference on Computer Science & Engineering Technology, Yerevan, Armenia, September 1997, p. 242).     

Social threshold aggregations

A problem of axiomatic construction of a social decision function is studied for the case when individual opinions of agents are given as m-graded preferences with arbitrary integer m ≥ 3. It is shown that the only rule satisfying the introduced axioms of Pairwise Compensation, Pareto Domination and Noncompensatory Threshold and Contraction is the threshold rule.     

Exordium for DNA Codes

We describe how deletion-correcting codes may be enhanced to yield codes with double-strand DNA-sequence codewords. This enhancement involves abstractions of the pertinent aspects of DNA; it nevertheless ensures specificity of binding for all pairs of single strands derived from its codewords—the key desideratum of DNA codes– i.e. with binding feasible only between reverse complementary strands. We defer discussing the combinatorial-optimization superincumbencies of code construction. Generalization of deletion similarity to an optimal sequence-alignment score could readily effect advantageous improvements (Kaderali, Master's Thesis, Informatics, U. Köln, 2001) but would render the combinatorics opaque. We mention motivating applications of DNA codes.     

Insider Trading,Stochastic Liquidity,and Equilibrium Prices

We extend Kyle's (1985) model of insider trading to the case where noise trading volatility follows a general stochastic process. We determine conditions under which, in equilibrium, price impact and price volatility are both stochastic, driven by shocks to uninformed volume even though the fundamental value is constant. The volatility of price volatility appears ‘excessive’ because insiders choose to trade more aggressively (and thus more information is revealed) when uninformed volume is higher and price impact is lower. This generates a positive relation between price volatility and trading volume, giving rise to an endogenous subordinate stochastic process for prices.