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Applications of structural equation models (SEMs) are often restricted to linear associations between variables. Maximum likelihood …

We propose to model the cause-specific cumulative incidence function of multivariate competing risks data using a random effects model …

Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model …

Family studies provide an important tool for understanding etiology of diseases, with the key aim of discovering evidence of family …

An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to …

We consider data from the Danish twin registry and aim to study in detail how lifetimes for twin-pairs are correlated. We consider …

Information theoretic methods are used to prove convergence in information divergence of reversible Markov chains. Also some ergodic …

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The 74HC165 and 74HC495 are useful integrated circuits for dealing with multiple digital inputs and outputs. In a recent project, I used the following prototype based on a variant of the above simple circuit schema:

The four 74HC495 ICs gives access to 2 x 16 bits accessible through IDC connectors controllable using just three pins on the microcontroller (DATA, CLOCK, LATCH). Similarly, two 74HC165 ICs gives access to 16 input bits through another IDC connector.

A small illustration on using the armadillo C++ linear algebra library for solving an ordinary differential equation of the form \[ X’(t) = F(t,X(t),U(t)).\]

The abstract super class Solver defines the methods solve (for approximating the solution in user-defined time-points) and solveint (for interpolating user-defined input functions on finer grid). As an illustration a simple Runge-Kutta solver is derived in the class RK4.

The first step is to define the ODE, here a simple one-dimensional ODE \(X’(t) = \theta\cdot\{U(t)-X(t)\}\) with a single input \(U(t)\):

rowvec dX(const rowvec &input, // time (first element) and additional input variables
	 const rowvec &x,     // state variables
	 const rowvec &theta) {   // parameters
  rowvec res = { theta(0)*theta(1)*(input(1)-x(0)) };
  return( res );

The ODE may then be solved using the following syntax

odesolver::RK4 MyODE(dX);
arma::mat res = MyODE.solve(input, init, theta);

with the step size defined implicitly by input (first column is the time variable and the following columns the optional different input variables) and boundary conditions defined by init.

Assume that two positive numbers are given, \(X\) and \(Y\), with unknown joint probability distribution \(P\), and \(X\neq Y\) a.s.

A player draws randomly one of the numbers and has to guess if the number is smaller or larger than the other unrevealed number, i.e., let \(U\sim Bernoulli(\tfrac{1}{2})\) independent of \(X, Y\), then the player sees \(Z_{1} = UX + (1-U)Y,\) and \(Z_{2} = (1-U)X + UY\) remains unseen.

A random guess (coin-flip) would due to the sampling \(U\), indepedently of \(F\), have probability \(\tfrac{1}{2}\) of correct guessing. The question is if we can find a better strategy?

The 74HC595: an 8-bit serial-in/serial or parallel-out shift register with a storage register and 3-state outputs.

If higher load is required there is also the TPIC6C595 (e.g., for driving LEDs), or it should be paired with for example ULN2803 or similar. For multiple inputs see the 74HC165.

Figure 1: Pin-out 74HC595.

Figure 1: Pin-out 74HC595.

The basic usage is to serially transfer a byte from a microcontroller to the IC. When latched the byte will then in parallel be available on output pins QA-QH (Q0-Q7).

\(\newcommand{\pr}{\mathbb{P}}\newcommand{\E}{\mathbb{E}}\) Relative risks (and risk differences) are collapsible and generally considered easier to interpret than odds-ratios. In a recent publication Richardson et al (JASA, 2017) proposed a new regression model for a binary exposure which solves the computational problems that are associated with using for example binomial regression with a log-link function (or identify link for the risk difference) to obtain such parameter estimates.

Let \(Y\) be the binary response, \(A\) binary exposure, and \(V\) a vector of covariates, then the target parameter is

\begin{align*} &\mathrm{RR}(v) = \frac{\pr(Y=1\mid A=1, V=v)}{\pr(Y=1\mid A=0, V=v)}. \end{align*}

Let \(p_a(V) = \pr(Y \mid A=a, V), a\in\{0,1\}\), then the idea is to posit a linear model for \[ \theta(v) = \log \big(RR(v)\big) \] and a nuisance model for the odds-product \[ \phi(v) = \log\left(\frac{p_{0}(v)p_{1}(v)}{(1-p_{0}(v))(1-p_{1}(v))}\right) \] noting that these two parameters are variation independent which can be from the below L’Abbé plot. Similarly, a model can be constructed for the risk-difference on the following scale \[\theta(v) = \mathrm{arctanh} \big(RD(v)\big).\]