Parameter estimation for the discretely observed fractional Ornstein–Uhlenbeck process and the Yuima R package

This paper proposes consistent and asymptotically Gaussian estimators for the parameters $\lambda , \sigma $ and $H$ of the discretely observed fractional Ornstein–Uhlenbeck process solution of the stochastic differential equation $d Y_t = -\lambda Y_t dt + \sigma d W_t^H$ , where $(W_t^H, t\ge 0)$ is the fractional Brownian motion. For the estimation of the drift $\lambda $ , the results are obtained only in the case when $\frac{1}{2} < H < \frac{3}{4}$ . This paper also provides ready-to-use software for the R statistical environment based on the YUIMA package.     

How Far Can Chemotactic Cross-diffusion Enforce Exceeding Carrying Capacities?

We consider nonnegative solutions of the Neumann initial-boundary value problem for the chemotaxis-growth system $$\begin{aligned} \left\{ \begin{array}{l} u_t=\varepsilon u_{xx} -(uv_x)_x +ru -\mu u^2, \qquad x\in \Omega , \ t>0, \\ 0=v_{xx}-v+u, \qquad x\in \Omega , \ t>0, \end{array} \right. \quad (\star ) \end{aligned}$$ in \(\Omega :=(0,L)\subset \mathbb {R}\) with \(L>0, \varepsilon >0, r\ge 0\) and \(\mu >0\) , along with the corresponding limit problem formally obtained upon taking \(\varepsilon \searrow 0\) . For the latter hyperbolic–elliptic problem, we establish results on local existence and uniqueness within an appropriate generalized solution concept. In this context we shall moreover derive an extensibility criterion involving the norm of \(u(\cdot ,t)\) in \(L^\infty (\Omega )\) . This will enable us to conclude that in this case \(\varepsilon =0\) ,

• if \(\mu \ge 1\) , then all solutions emanating from sufficiently regular initial data are global in time, whereas
• if \(\mu <1\) , then some solutions blow-up in finite time.

The latter will reveal that the original parabolic–elliptic problem ( \(\star \) ), though known to possess no such exploding solutions, exhibits the following property of dynamical structure generation: given any \(\mu \in (0,1)\) , one can find smooth bounded initial data with the property that for each prescribed number \(M>0\) the solution of ( \(\star \) ) will attain values above \(M\) at some time, provided that \(\varepsilon \) is sufficiently small. In particular, this means that the associated carrying capacity given by \(\frac{r}{\mu }\) can be exceeded during evolution to an arbitrary extent. We finally present some numerical simulations that illustrate this type of solution behavior and that, moreover, inter alia, indicate that achieving large population densities is a transient dynamical phenomenon occurring on intermediate time scales only.     

A class of semi-supervised support vector machines by DC programming

This paper investigate a class of semi-supervised support vector machines ( $\text{ S }^3\mathrm{VMs}$ ) with arbitrary norm. A general framework for the $\text{ S }^3\mathrm{VMs}$ was first constructed based on a robust DC (Difference of Convex functions) program. With different DC decompositions, DC optimization formulations for the linear and nonlinear $\text{ S }^3\mathrm{VMs}$ are investigated. The resulting DC optimization algorithms (DCA) only require solving simple linear program or convex quadratic program at each iteration, and converge to a critical point after a finite number of iterations. The effectiveness of proposed algorithms are demonstrated on some UCI databases and licorice seed near-infrared spectroscopy data. Moreover, numerical results show that the proposed algorithms offer competitive performances to the existing $\text{ S }^3\mathrm{VM}$ methods.     

Exact and approximate EM estimation of mutually exciting hawkes processes

Motivated by the availability of continuous event sequences that trace the social behavior in a population e.g. email, we believe that mutually exciting Hawkes processes provide a realistic and informative model for these sequences. For complex mutually exciting processes, the numerical optimization used for univariate self exciting processes may not provide stable estimates. Furthermore, convergence can be exceedingly slow, making estimation computationally expensive and multiple random restarts doubly so. We derive an expectation maximization algorithm for maximum likelihood estimation mutually exciting processes that is faster, more robust, and less biased than estimation based on numerical optimization. For an exponentially decaying excitement function, each EM step can be computed in a single $O(N)$ pass through the data, for $N$ observations, without requiring the entire dataset to be in memory. More generally, exact inference is $\Theta (N^{2})$ , but we identify some simple $\Theta (N)$ approximation strategies that seem to provide good estimates while reducing the computational cost.     

Some properties of a hypergeometric function which appear in an approximation problem

In this paper we consider properties and power expressions of the functions $f:(-1,1)\rightarrow \mathbb{R }$ and $f_L:(-1,1)\rightarrow \mathbb{R }$ , defined by $$\begin{aligned} f(x;\gamma )=\frac{1}{\pi }\int \limits _{-1}^1 \frac{(1+xt)^\gamma }{\sqrt{1-t^2}}\,\mathrm{d}t \quad \text{ and}\quad f_L(x;\gamma )=\frac{1}{\pi }\int \limits _{-1}^1 \frac{(1+xt)^\gamma \log (1+x t)}{\sqrt{1-t^2}}\,\mathrm{d}t, \end{aligned}$$ respectively, where $\gamma $ is a real parameter, as well as some properties of a two parametric real-valued function $D(\,\cdot \,;\alpha ,\beta ) :(-1,1) \rightarrow \mathbb{R }$ , defined by $$\begin{aligned} D(x;\alpha ,\beta )= f(x;\beta )f(x;-\alpha -1)- f(x;-\alpha )f(x;\beta -1),\quad \alpha ,\beta \in \mathbb{R }. \end{aligned}$$ The inequality of Turán type $$\begin{aligned} D(x;\alpha ,\beta )>0,\quad -1<x<1, \end{aligned}$$ for $\alpha +\beta >0$ is proved, as well as an opposite inequality if $\alpha +\beta <0$ . Finally, for the partial derivatives of $D(x;\alpha ,\beta )$ with respect to $\alpha $ or $\beta $ , respectively $A(x;\alpha ,\beta )$ and $B(x;\alpha ,\beta )$ , for which $A(x;\alpha ,\beta )=B(x;-\beta ,-\alpha )$ , some results are obtained. We mention also that some results of this paper have been successfully applied in various problems in the theory of polynomial approximation and some “truncated” quadrature formulas of Gaussian type with an exponential weight on the real semiaxis, especially in a computation of Mhaskar–Rahmanov–Saff numbers.     

\epsilon -Henig proper efficiency of set-valued optimization problems in real ordered linear spaces

The aim of this paper is to investigate \(\epsilon \) -Henig proper efficiency of set-valued optimization problems in linear spaces. Firstly, a new notion of \(\epsilon \) -Henig properly efficient point is introduced in linear spaces. Secondly, scalarization theorems of set-valued optimization problems are established in the sense of \(\epsilon \) -Henig proper efficiency. Finally, under the assumption of generalized cone subconvexlikeness, Lagrange multiplier theorems are obtained. Our results generalize some known results in the literature from topological spaces to linear spaces.     

Critical branching Brownian motion with absorption: survival probability

We consider branching Brownian motion on the real line with absorption at zero, in which particles move according to independent Brownian motions with the critical drift of \(-\sqrt{2}\) . Kesten (Stoch Process 7:9–47, 1978) showed that almost surely this process eventually dies out. Here we obtain upper and lower bounds on the probability that the process survives until some large time \(t\) . These bounds improve upon results of Kesten (Stoch Process 7:9–47, 1978), and partially confirm nonrigorous predictions of Derrida and Simon (EPL 78:60006, 2007).     

The Euler class group of a polynomial algebra with coefficients in a line bundle

Let $A$ be a commutative Noetherian ring and $P$ be a projective $A$ -module of rank $=(\text {dim}(A)-1)$ . An intriguing open question is to find the precise obstruction for $P$ to split as: $P\simeq Q\oplus A$ for some $A$ -module $Q$ . In this paper we settle this question when $A=R[T]$ for some ring $R$ containing the field of rationals and $P$ is a projective $A$ -module of rank $=\text {dim}(R)$ .     

Solving convex optimization with side constraints in a multi-class queue by adaptive c\mu rule

We study convex optimization problems with side constraints in a multi-class \(M/G/1\) queue with controllable service rates. In the simplest problem of optimizing linear costs with fixed service rate, the \(c\mu \) rule is known to be optimal. A natural question to ask is whether such simple policies exist for more complex control objectives. In this paper, combining the achievable region approach in queueing systems and the Lyapunov drift theory suitable to optimize renewal systems with time-average constraints, we show that convex optimization problems can be solved by variants of adaptive \(c\mu \) rules. These policies greedily re-prioritize job classes at the end of busy periods in response to past observed delays in each job class. Our method transforms the original problems into a new set of queue stability problems, and the adaptive \(c\mu \) rules are queue stable policies. An attractive feature of the adaptive \(c\mu \) rules is that they use limited statistics of the queue, where no statistics are required for the problem of satisfying average queueing delay in each job class.     

Uniqueness for Inverse Boundary Value Problems by Dirichlet-to-Neumann Map on Subboundaries

We consider inverse boundary value problems for elliptic equations of second order of determining coefficients by Dirichlet-to-Neumann map on subboundaries, that is, the mapping from Dirichlet data supported on subboundary ${\partial \Omega \setminus \Gamma_{-}}$ to Neumann data on subboundary ${\partial \Omega \setminus \Gamma_{+}}$ . First we prove uniqueness results in three dimensions under some conditions such as ${\overline{\Gamma_{+}\cup\Gamma_{-}}= \partial\Omega}$ Next we survey uniqueness results in two dimensions for various elliptic systems for arbitrarily given ${\Gamma_{-} = \Gamma_{+}}$ Our proof is based on complex geometric optics solutions which are constructed by a Carleman estimate.     

Asymptotic behaviour of first passage time distributions for Lévy processes

Let $X$ be a real valued Lévy process that is in the domain of attraction of a stable law without centering with norming function $c.$ As an analogue of the random walk results in Vatutin and Wachtel (Probab Theory Relat Fields 143(1–2):177–217, 2009) and Doney (Probab Theory Relat Fields 152(3–4):559–588, 2012), we study the local behaviour of the distribution of the lifetime $\zeta $ under the characteristic measure $\underline{n}$ of excursions away from $0$ of the process $X$ reflected in its past infimum, and of the first passage time of $X$ below $0,$ $T_{0}=\inf \{t>0:X_{t}<0\},$ under $\mathbb{P }_{x}(\cdot ),$ for $x>0,$ in two different regimes for $x,$ viz. $x=o(c(\cdot ))$ and $x>D c(\cdot ),$ for some $D>0.$ We sharpen our estimates by distinguishing between two types of path behaviour, viz. continuous passage at $T_{0}$ and discontinuous passage. In order to prove our main results we establish some sharp local estimates for the entrance law of the excursion process associated to $X$ reflected in its past infimum.     

Growth estimates for orbits of self-adjoint groups

Let $G$ denote a closed, connected, self-adjoint, noncompact subgroup of $GL(n,\mathbb R )$ , and let $d_{R}$ and $d_{L}$ denote respectively the right and left invariant Riemannian metrics defined by the canonical inner product on $M(n,\mathbb R ) = T_{I} GL(n,\mathbb R )$ . Let $v$ be a nonzero vector of $\mathbb R ^{n}$ such that the orbit $G(v)$ is unbounded in $\mathbb R ^{n}$ . Then the function $g \rightarrow d_{R}(g, G_{v})$ is unbounded, where $G_{v} = \{g \in G : g(v) = v \}$ , and we obtain algebraically defined upper and lower bounds $\lambda ^{+}(v)$ and $\lambda ^{-}(v)$ for the asymptotic behavior of the function $\frac{log|g(v)|}{d_{R}(g, G_{v})}$ as $d_{R}(g, G_{v}) \rightarrow \infty $ . The upper bound $\lambda ^{+}(v)$ is at most 1. The orbit $G(v)$ is closed in $\mathbb R ^{n} \Leftrightarrow \lambda ^{-}(w)$ is positive for some w $\in G(v)$ . If $G_{v}$ is compact, then $g \rightarrow |d_{R}(g,I) - d_{L}(g,I)|$ is uniformly bounded in $G$ , and the exponents $\lambda ^{+}(v)$ and $\lambda ^{-}(v)$ are sharp upper and lower asymptotic bounds for the functions $\frac{log|g(v)|}{d_{R}(g,I)}$ and $\frac{log|g(v)|}{d_{L}(g,I)}$ as $d_{R}(g,I) \rightarrow \infty $ or as $d_{L}(g,I) \rightarrow \infty $ . However, we show by example that if $G_{v}$ is noncompact, then there need not exist asymptotic upper and lower bounds for the function $\frac{log|g(v)|}{d_{L}(g, G_{v})}$ as $d_{L}(g, G_{v}) \rightarrow \infty $ . The results apply to representations of noncompact semisimple Lie groups $G$ on finite dimensional real vector spaces. We compute $\lambda ^{+}$ and $\lambda ^{-}$ for the irreducible, real representations of $SL(2,\mathbb R )$ , and we show that if the dimension of the $SL(2,\mathbb R )$ -module $V$ is odd, then $\lambda ^{+} = \lambda ^{-}$ on a nonempty open subset of $V$ . We show that the function $\lambda ^{-}$ is $K$ -invariant, where $K = O(n,\mathbb R ) \cap G$ . We do not know if $\lambda ^{-}$ is $G$ -invariant.     

Extinction profile of the logarithmic diffusion equation

Let ${N \geq 3}$ and u be the solution of u _t = Δ log u in ${\mathbb{R}^N \times (0, T)}$ with initial value u ₀ satisfying ${B_{k_1}(x, 0) \leq u_{0} \leq B_{k_2}(x, 0)}$ for some constants k ₁ > k ₂ > 0 where ${B_k(x, t) = 2(N - 2)(T - t)_{+}^{N/(N - 2)}/(k + (T - t)_{+}^{2/(N - 2)}|x|^{2})}$ is the Barenblatt solution for the equation and ${u_0 - B_{k_0} \in L^{1}(\mathbb{R}^{N})}$ for some constant k ₀ > 0 if ${N \geq 4}$ . We give a new different proof on the uniform convergence and ${L^1(\mathbb{R}^N)}$ convergence of the rescaled function ${\tilde{u}(x, s) = (T - t)^{-N/(N - 2)}u(x/(T - t)^{-1/(N - 2)}, t), s = -{\rm log}(T - t)}$ , on ${\mathbb{R}^N}$ to the rescaled Barenblatt solution ${\tilde{B}_{k_0}(x) = 2(N - 2)/(k_0 + |x|^{2})}$ for some k ₀ > 0 as ${s \rightarrow \infty}$ . When ${N \geq 4, 0 \leq u_0(x) \leq B_{k_0}(x, 0)}$ in ${\mathbb{R}^N}$ , and ${|u_0(x) - B_{k_0}(x, 0)| \leq f \in L^{1}(\mathbb{R}^{N})}$ for some constant k ₀ > 0 and some radially symmetric function f, we also prove uniform convergence and convergence in some weighted L ¹ space in ${\mathbb{R}^N}$ of the rescaled solution ${\tilde{u}(x, s)}$ to ${\tilde{B}_{k_0}(x)}$ as ${s \rightarrow \infty}$ .     

Existence of super-simple OA_{\lambda }(3, 5, v)^{\prime }s

It was proved recently that a super-simple orthogonal array (SSOA) of strength \(t\) and index \(\lambda \ge 2\) is equivalent to a minimum detecting array (DTA). In computer software tests in component-based systems, such a DTA can be used to generate test suites that are capable of locating \(d=\lambda -1\) \(t\) -way interaction faults and detect whether there are more than \(d\) interaction faults. It is proved in this paper that an SSOA of strength \(t=3\) , index \(\lambda \ge 2\) and degree \(k=5\) , or an SSOA \(_{\lambda }(3,5,v)\) , exists if and only if \(\lambda \le v\) excepting possibly a handful of cases.     

Accurate higher-order likelihood inference on P(Y<X)

The stress-strength reliability $R=P(Y<X)$ , where $X$ and $Y$ are independent continuous random variables, has obtained wide attention in many areas of application, such as in engineering statistics and biostatistics. Classical likelihood-based inference about $R$ has been widely examined under various assumptions on $X$ and $Y$ . However, it is well-known that first order inference can be inaccurate, in particular when the sample size is small or in presence of unknown parameters. The aim of this paper is to illustrate higher-order likelihood-based procedures for parametric inference in small samples, which provide accurate point estimators and confidence intervals for $R$ . The proposed procedures are illustrated under the assumptions of Gaussian and exponential models for $(X,Y)$ . Moreover, simulation studies are performed in order to study the accuracy of the proposed methodology, and an application to real data is discussed. An implementation of the proposed method in the R software is provided.     

Markov-modulated infinite-server queues with general service times

This paper analyzes several aspects of the Markov-modulated infinite-server queue. In the system considered (i) particles arrive according to a Poisson process with rate $\lambda _i$ when an external Markov process (“background process”) is in state $i$ , (ii) service times are drawn from a distribution with distribution function $F_i(\cdot )$ when the state of the background process (as seen at arrival) is $i$ , (iii) there are infinitely many servers. We start by setting up explicit formulas for the mean and variance of the number of particles in the system at time $t\ge 0$ , given the system started empty. The special case of exponential service times is studied in detail, resulting in a recursive scheme to compute the moments of the number of particles at an exponentially distributed time, as well as their steady-state counterparts. Then we consider an asymptotic regime in which the arrival rates are sped up by a factor $N$ , and the transition times by a factor $N^{1+\varepsilon }$ (for some $\varepsilon >0$ ). Under this scaling it turns out that the number of customers at time $t\ge 0$ obeys a central limit theorem; the convergence of the finite-dimensional distributions is proven.     

Hadamard well-posedness for a set-valued optimization problem

In this paper, we introduce a kind of Hadamard well-posedness for a set-valued optimization problem. By virtue of a scalarization function, we obtain some relationships between weak ${(\varepsilon, e)}$ -minimizers of the set-valued optimization problem and ${\varepsilon}$ -approximate solutions of a scalar optimization problem. Then, we establish a scalarization theorem of P.K. convergence for sequences of set-valued mappings. Based on these results, we also derive a sufficient condition of Hadamard well-posedness for the set-valued optimization problem.     

Subgame perfect equilibria in stopping games

Stopping games (without simultaneous stopping) are multi-player sequential games in which at every stage one of the players is chosen according to a stochastic process, and that player decides whether to continue the interaction or to stop it, whereby the terminal payoff vector is obtained by another stochastic process. We prove that if the payoff process is integrable, a $\delta $ -approximate subgame perfect ${\epsilon }$ -equilibrium exists for every $\delta ,\epsilon >0$ ; that is, there exists a strategy profile that is an ${\epsilon }$ -equilibrium in all subgames, except possibly in a set of subgames that occurs with probability at most $\delta $ (even after deviation by some of the players).     

Problems with resource allocation constraints and optimization over the efficient set

The paper studies a nonlinear optimization problem under resource allocation constraints. Using quasi-gradient duality it is shown that the feasible set of the problem is a singleton (in the case of a single resource) or the set of Pareto efficient solutions of an associated vector maximization problem (in the case of $k>1$ resources). As a result, a nonlinear optimization problem under resource allocation constraints reduces to an optimization over the efficient set. The latter problem can further be converted into a quasiconvex maximization over a compact convex subset of $\mathbb{R }^k_+.$ Alternatively, it can be approached as a bilevel program and converted into a monotonic optimization problem in $\mathbb{R }^k_+.$ In either approach the converted problem falls into a common class of global optimization problems for which several practical solution methods exist when the number $k$ of resources is relatively small, as it often occurs.